August 2018, 1(3): 201-253. doi: 10.3934/mfc.2018010

Influence analysis: A survey of the state-of-the-art

1. 

Kennesaw State University, 1100 South Marietta Pkwy, Marietta, GA, 30060, USA

2. 

Georgia State University, 25 Park place, Atlanta, GA, 30303, USA

* Corresponding author: Meng Han

Received  December 2017 Revised  February 2018 Published  July 2018

Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference.

Citation: Meng Han, Yingshu Li. Influence analysis: A survey of the state-of-the-art. Mathematical Foundations of Computing, 2018, 1 (3) : 201-253. doi: 10.3934/mfc.2018010
References:
[1]

I. Abraham, S. Chechik, D. Kempe and A. Slivkins, Low-distortion inference of latent similarities from a multiplex social network, SIAM J. Comput., 44 (2015), 617–668, arXiv: 1202.0922. doi: 10.1137/130949191.

[2]

R. Agrawal, Nature of information, people, and relationships in digital social networks.

[3]

R. Agrawal, M. Potamias and E. Terzi, Learning the nature of information in social networks, 2012.

[4]

C. Ai, M. Han, J. Wang and M. Yan, An efficient social event invitation framework based on historical data of smart devices, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,229–236. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.44.

[5]

H. Albinali, M. Han, J. Wang, H. Gao and Y. Li, The roles of social network mavens, in The 12th International Conference on Mobile Ad-hoc and Sensor Networks (MSN 2016), 2016, 1–12. doi: 10.1109/MSN.2016.009.

[6]

A. Anagnostopoulos, R. Kumar and M. Mahdian, Influence and correlation in social networks, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Las Vegas, Nevada, USA, 2008, 7–15. doi: 10.1145/1401890.1401897.

[7]

C. AnagnostopoulosS. Hadjiefthymiades and E. Zervas, An analytical model for multi-epidemic information dissemination, J. Parallel Distrib. Comput., 71 (2011), 87-104, 1891295. doi: 10.1016/j.jpdc.2010.08.010.

[8]

T. C. AntonucciK. J. Ajrouch and K. S. Birditt, The convoy model: Explaining social relations from a multidisciplinary perspective, The Gerontologist, 54 (2014), 82-92. doi: 10.1093/geront/gnt118.

[9]

S. E. Asch, Opinions and social pressure, Readings about the social animal, 193 (1955), 17-26. doi: 10.1038/scientificamerican1155-31.

[10]

C. C. I. AslayW. LuF. BonchiA. Goyal and L. V. S. Lakshmanan, Viral marketing meets social advertising: Ad allocation with minimum regret, Proceedings of the VLDB Endowment VLDB Endowment Hompage Archive, 8 (2015), 814-825. doi: 10.14778/2752939.2752950.

[11]

D. B. BahrR. C. BrowningH. R. Wyatt and J. O. Hill, Exploiting social networks to mitigate the obesity epidemic, Obesity (Silver Spring), 17 (2009), 723-728. doi: 10.1038/oby.2008.615.

[12]

E. Bakshy, D. Eckles, R. Yan and I. Rosenn, Social influence in social advertising: Evidence from field experiments, in Proceedings of the 13th ACM Conference on Electronic Commerce, ACM, Valencia, Spain, 2012,146–161. doi: 10.1145/2229012.2229027.

[13]

E. Bakshy, J. M. Hofman, W. A. Mason and D. J. Watts, Everyone's an influencer: quantifying influence on twitter, in Proceedings of the fourth ACM international conference on Web search and data mining, ACM, Hong Kong, China, 2011, 65–74. doi: 10.1145/1935826.1935845.

[14]

E. Bakshy, I. Rosenn, C. Marlow and L. Adamic, The role of social networks in information diffusion, in Proceedings of the 21st International Conference on World Wide Web, ACM, Lyon, France, 2012,519–528. doi: 10.1145/2187836.2187907.

[15]

N. Barbieri and F. Bonchi, Influence maximization with viral product design, Proceedings of the 2014 SIAM International Conference on Data Mining, 2014, p9. doi: 10.1137/1.9781611973440.7.

[16]

N. Barbieri, F. Bonchi and G. Manco, Topic-aware social influence propagation models, in Proceedings of the 2012 IEEE 12th International Conference on Data Mining, IEEE Computer Society, 2012, 81–90.

[17]

S. Bhagat, A. Goyal and L. V. S. Lakshmanan, Maximizing product adoption in social networks, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, Seattle, Washington, USA, 2012,603–612. doi: 10.1145/2124295.2124368.

[18]

S. Bharathi, D. Kempe and M. Salek, Competitive influence maximization in social networks, in Internet and Network Economics, Springer, 2007,306–311. doi: 10.1007/978-3-540-77105-0_31.

[19]

K. Bhawalkar, S. Gollapudi and K. Munagala, Coevolutionary opinion formation games, STOC'13Proceedings of the 2013 ACM Symposium on Theory of Computing, 41–50, ACM, New York, 2013. doi: 10.1145/2488608.2488615.

[20]

F. Bonchi, Influence propagation in social networks: A data mining perspective, IEEE Intelligent Informatics Bulletin, 12 (2011), 8-16. doi: 10.1109/WI-IAT.2011.292.

[21]

C. Borgs, M. Brautbar, J. Chayes and B. Lucier, Maximizing social influence in nearly optimal time, Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 946–957, ACM, New York, 2014. doi: 10.1137/1.9781611973402.70.

[22]

A. Borodin, Y. Filmus and J. Oren, Threshold models for competitive influence in social networks, in Proceedings of the 6th international conference on Internet and network economics, Springer-Verlag, Stanford, CA, USA, 2010,539–550. doi: 10.1007/978-3-642-17572-5_48.

[23]

S. BourigaultC. LagnierS. LamprierL. Denoyer and P. Gallinari, Learning social network embeddings for predicting information diffusion, WSDM '14 Proceedings of the 7th ACM International Conference on Web Search and Data Mining, (2014), 393-402. doi: 10.1145/2556195.2556216.

[24]

C. Budak and R. Agrawal, On participation in group chats on twitter, 2013,165–176.

[25]

J. T. Cacioppo, J. H. Fowler and N. A. Christakis, Alone in the crowd: the structure and spread of loneliness in a large social network., Journal of Personality and Social Psychology, 97 (2009), 977.

[26]

J. L. Z. Cai, M. Yan and Y. Li, Using crowdsourced data in location-based social networks to explore influence maximization, in Computer Communications, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on, IEEE, 2016, 1–9. doi: 10.1109/INFOCOM.2016.7524471.

[27]

Z. Cai, Z. He, X. Guan and Y. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, (2016), p1. doi: 10.1109/TDSC.2016.2613521.

[28]

J. Cannarella and J. A. Spechler, Epidemiological modeling of online social network dynamics, arXiv preprint, arXiv: 1401.4208.

[29]

T. CarnesC. NagarajanS. M. Wild and A. Van Zuylen, Maximizing influence in a competitive social network: a follower's perspective, ICEC '07 Proceedings of the Ninth International Conference on Electronic Commerce, (2007), 351-360. doi: 10.1145/1282100.1282167.

[30]

M. ChaH. HaddadiF. Benevenuto and P. K. Gummadi, Measuring user influence in twitter: The million follower fallacy, ICWSM, 10 (2010), 10-17.

[31]

M. Cha, A. Mislove and K. P. Gummadi, A measurement-driven analysis of information propagation in the flickr social network, 2009,721–730.

[32]

M. Cha, A. Mislove and K. P. Gummadi, A measurement-driven analysis of information propagation in the flickr social network, in Proceedings of the 18th International Conference on World Wide Web, ACM, Madrid, Spain, 2009,721–730. doi: 10.1145/1526709.1526806.

[33]

Y. Chang, X. Wang, Q. Mei and Y. Liu, Towards twitter context summarization with user influence models, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, Rome, Italy, 2013,527–536. doi: 10.1145/2433396.2433464.

[34]

V. Chaoji, S. Ranu, R. Rastogi and R. Bhatt, Recommendations to boost content spread in social networks, in Proceedings of the 21st International Conference on World Wide Web, ACM, Lyon, France, 2012,529–538. doi: 10.1145/2187836.2187908.

[35]

L. Chen, X. Li and J. Han, Medrank: discovering influential medical treatments from literature by information network analysis, in Proceedings of the Twenty-Fourth Australasian Database Conference, Australian Computer Society, Inc., Adelaide, Australia, 2013, 3–12.

[36]

S. ChenJ. FanG. LiJ. FengK.-l. Tan and J. Tang, Online topic-aware influence maximization, Proceedings of the VLDB Endowment, 8 (2015), 666-677. doi: 10.14778/2735703.2735706.

[37]

W. ChenA. CollinsR. CummingsT. KeZ. LiuD. RinconX. SunY. WangW. Wei and Y. Yuan, Influence maximization in social networks when negative opinions may emerge and propagate, Proceedings of the 2011 SIAM International Conference on Data Mining, (2011), 379-390. doi: 10.1137/1.9781611972818.33.

[38]

W. Chen, T. Lin and C. Yang, Efficient topic-aware influence maximization using preprocessing, CoRR, abs/1403.0057.

[39]

W. ChenZ. LiuX. Sun and Y. Wang, A game-theoretic framework to identify overlapping communities in social networks, Data Min. Knowl. Discov., 21 (2010), 224-240. doi: 10.1007/s10618-010-0186-6.

[40]

W. Chen, P. Lu, X. Sun, B. Tang, Y. Wang and Z. A. Zhu, Optimal pricing in social networks with incomplete information, in Internet and Network Economics, Springer, 2011, 49–60.

[41]

W. Chen, W. Lu and N. Zhang, Time-critical influence maximization in social networks with time-delayed diffusion process, 2012.

[42]

W. ChenC. Wang and Y. Wang, Scalable influence maximization for prevalent viral marketing in large-scale social networks, Data Min. Knowl. Discov., 25 (2012), 545-576. doi: 10.1007/s10618-012-0262-1.

[43]

W. Chen, Y. Wang and S. Yang, Efficient influence maximization in social networks, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Paris, France, 2009,199–208. doi: 10.1145/1557019.1557047.

[44]

W. Chen, Y. Yuan and L. Zhang, Scalable influence maximization in social networks under the linear threshold model, in Proceedings of the 2010 IEEE International Conference on Data Mining, IEEE Computer Society, 2010, 88–97. doi: 10.1109/ICDM.2010.118.

[45]

Y. -C. Chen, W. -Y. Zhu, W. -C. Peng, W. -C. Lee and S. -Y. Lee, Cim: community-based influence maximization in social networks, ACM Transactions on Intelligent Systems and Technology (TIST), 5 (2014), Article No. 25. doi: 10.1145/2532549.

[46]

S. ChengH. ShenJ. HuangW. Chen and X. Cheng, Imrank: Influence maximization via finding self-consistent ranking, SIGIR '14 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, (2014), 475-484. doi: 10.1145/2600428.2609592.

[47]

N. A. Christakis and J. H. Fowler, The spread of obesity in a large social network over 32 years, N Engl J Med, 357 (2007), 370-379. doi: 10.1056/NEJMsa066082.

[48]

N. A. Christakis and J. H. Fowler, The collective dynamics of smoking in a large social network, New England Journal of Medicine, 358 (2008), 2249-2258. doi: 10.1056/NEJMsa0706154.

[49]

P. Clifford and A. Sudbury, A model for spatial conflict, Biometrika, 60 (1973), 581-588. doi: 10.1093/biomet/60.3.581.

[50]

L. CorazziniF. PavesiB. Petrovich and L. Stanca, Influential listeners: An experiment on persuasion bias in social networks, European Economic Review, 56 (2012), 1276-1288.

[51]

D. Cosley, D. P. Huttenlocher, J. M. Kleinberg, X. Lan and S. Suri, Sequential influence models in social networks., ICWSM, 10 (2010), 26.

[52]

D. M. Cutler and E. L. Glaeser, Social interactions and smoking, Technical report, National Bureau of Economic Research, (2007), 1-28. doi: 10.3386/w13477.

[53]

A. DasS. Gollapudi and K. Munagala, Modeling opinion dynamics in social networks, WSDM '14 Proceedings of the 7th ACM International Conference on Web Search and Data Mining, (2014), 403-412. doi: 10.1145/2556195.2559896.

[54]

A. Das, S. Gollapudi, R. Panigrahy and M. Salek, Debiasing social wisdom, 2013,500–508.

[55]

A. Datta, A. Datta, A. D. Procaccia and Y. Zick, Influence in classification via cooperative game theory, arXiv preprint, arXiv: 1505.00036.

[56]

I. de Sola Pool and M. Kochen, Contacts and influence, Social Networks, 1 (1979), 5-51. doi: 10.1016/0378-8733(78)90011-4.

[57]

E. D. Demaine, M. Hajiaghayi, H. Mahini, D. L. Malec, S. Raghavan, A. Sawant and M. Zadimoghadam, How to influence people with partial incentives, in World Wide Web Conferences, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 2014,937–948. doi: 10.1145/2566486.2568039.

[58]

T. N. Dinh, D. T. Nguyen and M. T. Thai, Cheap, easy, and massively effective viral marketing in social networks: truth or fiction?, in Proceedings of the 23rd ACM conference on Hypertext and Social Media, ACM, Milwaukee, Wisconsin, USA, 2012,165–174. doi: 10.1145/2309996.2310024.

[59]

P. S. DoddsR. Muhamad and D. J. Watts, An experimental study of search in global social networks, Science, 301 (2003), 827-829. doi: 10.1126/science.1081058.

[60]

P. Domingos and M. Richardson, Mining the network value of customers, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, California, 2001, 57–66. doi: 10.1145/502512.502525.

[61]

Y. DongR. A. Johnson and N. V. Chawla, Will this paper increase your h-index?: Scientific impact prediction, Machine Learning and Knowledge Discovery in Databases, (2015), 259-263. doi: 10.1007/978-3-319-23461-8_26.

[62]

Z. Duan, W. Li and Z. Cai, Distributed auctions for task assignment and scheduling in mobile crowdsensing systems, in Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on, IEEE, 2017,635–644. doi: 10.1109/ICDCS.2017.121.

[63]

Z. Duan, M. Yan, Z. Cai, X. Wang, M. Han and Y. Li, Truthful incentive mechanisms for social cost minimization in mobile crowdsourcing systems, Sensors, 16 (2016), 481. doi: 10.3390/s16040481.

[64]

I. Eleta, Multilingual use of twitter: Social networks and language choice, in ACM Conference on Computer-Supported Cooperative Work and Social Computing, ACM, New York, NY, USA, 2012,363–366. doi: 10.1145/2141512.2141621.

[65]

E. Even-Dar and A. Shapira, A note on maximizing the spread of influence in social networks, in Internet and Network Economics, Springer, 2007,281–286. doi: 10.1007/978-3-540-77105-0_27.

[66]

Y. Fan and C. R. Shelton, Learning continuous-time social network dynamics, 2009,161–168.

[67]

K. FengG. CongS. S. Bhowmick and S. Ma, In search of influential event organizers in online social networks, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2014), 63-74. doi: 10.1145/2588555.2612173.

[68]

J. H. Fowler, N. A. Christakis, Steptoe and D. Roux, Dynamic spread of happiness in a large social network: Longitudinal analysis of the framingham heart study social network, BMJ: British Medical Journal, 23–27.

[69]

L. C. Freeman, A set of measures of centrality based on betweenness, Sociometry, 40 (1977), 35-41. doi: 10.2307/3033543.

[70]

P. J. GiabbanelliA. AlimadadV. Dabbaghian and D. T. Finegood, Modeling the influence of social networks and environment on energy balance and obesity, Journal of Computational Science, 3 (2012), 17-27.

[71]

A. Goyal, F. Bonchi and L. V. S. Lakshmanan, Learning influence probabilities in social networks, in Proceedings of the Third ACM International Conference on Web Search and Data Mining, ACM, New York, New York, USA, 2010,241–250. doi: 10.1145/1718487.1718518.

[72]

A. GoyalF. Bonchi and L. V. S. Lakshmanan, A data-based approach to social influence maximization, Proc. VLDB Endow., 5 (2011), 73-84. doi: 10.14778/2047485.2047492.

[73]

A. Goyal, F. Bonchi, L. V. Lakshmanan and S. Venkatasubramanian, Approximation analysis of influence spread in social networks, arXiv preprint, arXiv: 1008.2005.

[74]

A. GoyalF. BonchiL. V. Lakshmanan and S. Venkatasubramanian, On minimizing budget and time in influence propagation over social networks, Social Network Analysis and Mining, 3 (2013), 179-192. doi: 10.1007/s13278-012-0062-z.

[75]

A. Goyal and L. V. S. Lakshmanan, Recmax: Exploiting recommender systems for fun and profit, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, 1294–1302. doi: 10.1145/2339530.2339731.

[76]

A. Goyal, W. Lu and L. V. S. Lakshmanan, Celf++: Optimizing the greedy algorithm for influence maximization in social networks, in Proceedings of the 20th International Conference Companion on World Wide Web, Proceedings of the 20th international conference companion on World wide web, ACM, Hyderabad, India, 2011, 47–48. doi: 10.1145/1963192.1963217.

[77]

A. Goyal, W. Lu and L. V. S. Lakshmanan, Simpath: An efficient algorithm for influence maximization under the linear threshold model, in Proceedings of the 2011 IEEE 11th International Conference on Data Mining, IEEE Computer Society, 2011,211–220. doi: 10.1109/ICDM.2011.132.

[78]

S. Goyal and M. Kearns, Competitive contagion in networks, STOC'12Proceedings of the 2012 ACM Symposium on Theory of Computing, (2012), 759-774. doi: 10.1145/2213977.2214046.

[79]

M. Grabisch and A. Rusinowska, A model of influence in a social network, Theory and Decision, 69 (2010), 69-96. doi: 10.1007/s11238-008-9109-z.

[80]

M. Granovetter, The strength of weak ties, American Journal of Sociology, 78 (1973), l.

[81]

D. Gruhl, R. Guha, D. Liben-Nowell and A. Tomkins, Information diffusion through blogspace, 2004,491–501.

[82]

A. GuilleH. HacidC. E. C. Favre and D. A. Zighed, Information diffusion in online social networks: A survey, ACM SIGMOD Record, 42 (2013), 17-28. doi: 10.1145/2503792.2503797.

[83]

B. HanP. HuiV. A. KumarM. V. MaratheJ. Shao and A. Srinivasan, Mobile data offloading through opportunistic communications and social participation, Mobile Computing, IEEE Transactions on, 11 (2012), 821-834. doi: 10.1109/TMC.2011.101.

[84]

B. Han and A. Srinivasan, Your friends have more friends than you do: identifying influential mobile users through random walks, in Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing, ACM, Hilton Head, South Carolina, USA, 2012, 5–14. doi: 10.1145/2248371.2248376.

[85]

M. HanZ. DuanC. AiF. W. LybargerY. Li and A. G. Bourgeois, Time constraint influence maximization algorithm in the age of big data, International Journal of Computational Science and Engineering, 15 (2017), 165-175. doi: 10.1504/IJCSE.2017.087401.

[86]

M. Han, Z. Duan and Y. Li, Privacy issues for transportation cyber physical systems, in Secure and Trustworthy Transportation Cyber-Physical Systems, Springer, Singapore, 2017, 67–86. doi: 10.1007/978-981-10-3892-1_4.

[87]

M. HanQ. HanL. LiJ. Li and Y. Li, Maximizing influence in sensed heterogenous social network with privacy preservation, International Journal of Sensor Networks, (2017), 1-11. doi: 10.1504/IJSNET.2017.10007412.

[88]

M. Han, J. Li, Z. Cai and Q. Han, Privacy reserved influence maximization in gps-enabled cyber-physical and online social networks, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,284–292. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.51.

[89]

M. HanJ. Li and Z. Zou, Finding k close subgraphs in an uncertain graph, Jisuanji Kexue yu Tansuo, 5 (2011), 791-803.

[90]

M. HanL. LiX. PengZ. Hong and M. Li, Information privacy of cyber transportation system: Opportunities and challenges, RIIT '17 Proceedings of the 6th Annual Conference on Research in Information Technology, (2017), 23-28. doi: 10.1145/3125649.3125652.

[91]

M. HanL. LiY. XieJ. WangZ. DuanJ. Li and M. Yan, Cognitive approach for location privacy protection, IEEE Access, 6 (2018), 13466-13477. doi: 10.1109/ACCESS.2018.2805464.

[92]

M. Han, Y. Liang, Z. Duan and Y. Wang, Mining public business knowledge: A case study in sec's edgar, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,393–400. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.65.

[93]

M. HanJ. WangM. YanC. AiZ. Duan and Z. Hong, Near-complete privacy protection: Cognitive optimal strategy in location-based services, Procedia Computer Science, 129 (2018), 298-304. doi: 10.1016/j.procs.2018.03.079.

[94]

M. HanM. YanZ. Cai and Y. Li, An exploration of broader influence maximization in timeliness networks with opportunistic selection, Journal of Network and Computer Applications, 63 (2016), 39-49. doi: 10.1016/j.jnca.2016.01.004.

[95]

M. Han, M. Yan, Z. Cai, Y. Li, X. Cai and J. Yu, Influence maximization by probing partial communities in dynamic online social networks, Transactions on Emerging Telecommunications Technologies, 28 (2017), e3054. doi: 10.1002/ett.3054.

[96]

M. Han, M. Yan, J. Li, S. Ji and Y. Li, Generating uncertain networks based on historical network snapshots, in COCOON, 2013,747–758. doi: 10.1007/978-3-642-38768-5_68.

[97]

M. HanM. YanJ. LiS. Ji and Y. Li, Neighborhood-based uncertainty generation in social networks, Journal of Combinatorial Optimization, 28 (2014), 561-576. doi: 10.1007/s10878-013-9684-y.

[98]

M. HanW. Zhang and J.-Z. Li, Raking: An efficient k-maximal frequent pattern mining algorithm on uncertain graph database, Jisuanji Xuebao(Chinese Journal of Computers), 33 (2010), 1387-1395. doi: 10.3724/SP.J.1016.2010.01387.

[99]

R. A. Hanneman and M. Riddle, Introduction to social network methods, 2005.

[100]

D. Hatano, T. Fukunaga, T. Maehara and K. -i. Kawarabayashi, Lagrangian decomposition algorithm for allocating marketing channels, 2015.

[101]

J. He, J. Hopcroft, H. Liang, S. Suwajanakorn and L. Wang, Detecting the structure of social networks using (α, β)-communities, in Algorithms and Models for the Web Graph, Springer, 6732 (2011), 26–37. doi: 10.1007/978-3-642-21286-4_3.

[102]

X. He and D. Kempe, Price of anarchy for the n-player competitive cascade game with submodular activation functions, in Web and Internet Economics, Springer, 2013,232–248. doi: 10.1007/978-3-642-45046-4_20.

[103]

X. He and D. Kempe, Stability of influence maximization, KDD '14 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), 1256-1265. doi: 10.1145/2623330.2623746.

[104]

X. He, G. Song, W. Chen and Q. Jiang, Influence blocking maximization in social networks under the competitive linear threshold model, 2012,463–474.

[105]

Z. He, Z. Cai and X. Wang, Modeling propagation dynamics and developing optimized countermeasures for rumor spreading in online social networks, in Distributed Computing Systems (ICDCS), 2015 IEEE 35th International Conference on, IEEE, 2015,205–214. doi: 10.1109/ICDCS.2015.29.

[106]

Z. HeZ. CaiJ. YuX. WangY. Sun and Y. Li, Cost-efficient strategies for restraining rumor spreading in mobile social networks, IEEE Transactions on Vehicular Technology, 66 (2017), 2789-2800. doi: 10.1109/TVT.2016.2585591.

[107]

M. HeidariM. Asadpour and H. Faili, Smg: Fast scalable greedy algorithm for influence maximization in social networks, Physica A: Statistical Mechanics and its Applications, 420 (2015), 124-133. doi: 10.1016/j.physa.2014.10.088.

[108]

C. Hoede and R. R. Bakker, A theory of decisional power, Journal of Mathematical Sociology, 8 (1982), 309-322. doi: 10.1080/0022250X.1982.9989927.

[109]

J. HopcroftT. Lou and J. Tang, Who will follow you back?: Reciprocal relationship prediction, CIKM '11 Proceedings of the 20th ACM International Conference on Information and Knowledge Management, (2011), 1137-1146. doi: 10.1145/2063576.2063740.

[110]

J. HuK. MengX. ChenC. Lin and J. Huang, Analysis of influence maximization in large-scale social networks, SIGMETRICS Perform. Eval. Rev., 41 (2014), 78-81. doi: 10.1145/2627534.2627559.

[111]

X. Hu, L. Tang, J. Tang and H. Liu, Exploiting social relations for sentiment analysis in microblogging, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, Rome, Italy, 2013,537–546. doi: 10.1145/2433396.2433465.

[112]

Z. HuJ. YaoB. Cui and E. Xing, Community level diffusion extraction, SIGMOD '15 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, (2015), 1555-1569. doi: 10.1145/2723372.2723737.

[113]

H. Huang, J. Tang, S. Wu, L. Liu and Others, Mining triadic closure patterns in social networks, WWW '14 Companion Proceedings of the 23rd International Conference on World Wide Web, 2014,499–504. doi: 10.1145/2567948.2576940.

[114]

J. -P. Huang, C. -Y. Wang and H. -Y. Wei, Strategic information diffusion through online social networks, in Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ACM, Barcelona, Spain, 2011, Article No. 88, 5pp. doi: 10.1145/2093698.2093786.

[115]

J. Huang, X. -Q. Cheng, H. -W. Shen, T. Zhou and X. Jin, Exploring social influence via posterior effect of word-of-mouth recommendations, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, Seattle, Washington, USA, 2012,573–582. doi: 10.1145/2124295.2124365.

[116]

J. E. L. Iribarren and E. Moro, Impact of human activity patterns on the dynamics of information diffusion, Physical Review Letters, 103 (2009), 038702. doi: 10.1103/PhysRevLett.103.038702.

[117]

J. H. JanssenW. A. IJsselsteijn and J. H. Westerink, How affective technologies can influence intimate interactions and improve social connectedness, International Journal of Human-Computer Studies, 72 (2014), 33-43. doi: 10.1016/j.ijhcs.2013.09.007.

[118]

S. Ji, Z. Cai, M. Han and R. Beyah, Whitespace measurement and virtual backbone construction for cognitive radio networks: From the social perspective, in Sensing, Communication, and Networking (SECON), 2015 12th Annual IEEE International Conference on, IEEE, 2015,435–443. doi: 10.1109/SAHCN.2015.7338344.

[119]

F. Jiang, S. Jin, Y. Wu and J. Xu, A uniform framework for community detection via influence maximization in social networks, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014. doi: 10.1109/ASONAM.2014.6921556.

[120]

A. P. JoshiM. Han and Y. Wang, A survey on security and privacy issues of blockchain technology, Mathematical Foundations of Computing, 1 (2018), 121-147.

[121]

K. Jung, W. Heo and W. Chen, Irie: A scalable influence maximization algorithm for independent cascade model and its extensions, arXiv preprint, arXiv: 1111.4795.

[122]

D. Kempe, J. Kleinberg, S. Oren and A. Slivkins, Selection and influence in cultural dynamics, in Proceedings of the fourteenth ACM conference on Electronic commerce, ACM, Philadelphia, Pennsylvania, USA, 2013,585–586. doi: 10.1145/2492002.2482566.

[123]

D. KempeJ. Kleinberg and E. V. Tardos, Influential nodes in a diffusion model for social networks, Automata, Languages and Programming, (2005), 1127-1138. doi: 10.1007/11523468_91.

[124]

D. KempeJ. Kleinberg and V. Tardos, Maximizing the spread of influence through a social network, Theory Comput., 11 (2015), 105-147. doi: 10.4086/toc.2015.v011a004.

[125]

S. Khanna and B. Lucier, Influence maximization in undirected networks, Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 1482–1496, ACM, New York, 2014. doi: 10.1137/1.9781611973402.109.

[126]

Y. A. Kim and J. Srivastava, Impact of social influence in e-commerce decision making, in Proceedings of the Ninth International Conference on Electronic Commerce, ACM, Minneapolis, MN, USA, 2007,293–302. doi: 10.1145/1282100.1282157.

[127]

M. KimuraK. SaitoR. Nakano and H. Motoda, Extracting influential nodes on a social network for information diffusion, Data Min. Knowl. Discov., 20 (2010), 70-97. doi: 10.1007/s10618-009-0150-5.

[128]

F. Kooti, W. A. Mason, K. P. Gummadi and M. Cha, Predicting emerging social conventions in online social networks, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, 2012,445–454, 2396820. doi: 10.1145/2396761.2396820.

[129]

J. Kostka, Y. A. Oswald and R. Wattenhofer, Word of mouth: Rumor dissemination in social networks, in Structural Information and Communication Complexity, Springer, 5058 (2008), 185–196. doi: 10.1007/978-3-540-69355-0_16.

[130]

R. KumarJ. NovakP. Raghavan and A. Tomkins, On the bursty evolution of blogspace, WWW '03 Proceedings of the 12th international conference on World Wide Web, (2003), 568-576. doi: 10.1145/775152.775233.

[131]

R. KumarJ. Novak and A. Tomkins, Structure and evolution of online social networks, KDD '06 Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2006), 611-617. doi: 10.1145/1150402.1150476.

[132]

O. Kwon and Y. Wen, An empirical study of the factors affecting social network service use, Computers in Human Behavior, 26 (2010), 254-263. doi: 10.1016/j.chb.2009.04.011.

[133]

T. La Fond and J. Neville, Randomization tests for distinguishing social influence and homophily effects, WWW '10 Proceedings of the 19th International Conference on World Wide Web, (2010), 601-610. doi: 10.1145/1772690.1772752.

[134]

M. Lahiri, A. S. Maiya, R. Sulo, Habiba and T. Y. B. Wolf, The impact of structural changes on predictions of diffusion in networks, in Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, IEEE Computer Society, 2008,939–948. doi: 10.1109/ICDMW.2008.92.

[135]

X. N. LamT. VuT. D. Le and A. D. Duong, Addressing cold-start problem in recommendation systems, CUIMC '08 Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, (2008), 208-211. doi: 10.1145/1352793.1352837.

[136]

I. Leftheriotis and M. N. Giannakos, Using social media for work: Losing your time or improving your work?, Computers in Human Behavior, 31 (2014), 134-142. doi: 10.1016/j.chb.2013.10.016.

[137]

S. LeiS. ManiuL. MoR. Cheng and P. Senellart, Online influence maximization, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 645-654. doi: 10.1145/2783258.2783271.

[138]

J. LeskovecL. Backstrom and J. Kleinberg, Meme-tracking and the dynamics of the news cycle, KDD '09 Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2009), 497-506. doi: 10.1145/1557019.1557077.

[139]

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen and N. Glance, Costeffective outbreak detection in networks, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Jose, California, USA, 2007,420–429. doi: 10.1145/1281192.1281239.

[140]

J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance and M. Hurst, Information propagation and network evolution on the web, DA Project, Machine Learning Dept. Carnegie Mellon University.

[141]

J. LeskovecM. McGlohonC. FaloutsosN. S. Glance and M. Hurst, Patterns of cascading behavior in large blog graphs, Proceedings of the 2007 SIAM International Conference on Data Miningvol, 7 (2007), 551-556. doi: 10.1137/1.9781611972771.60.

[142]

K. LewisM. Gonzalez and J. Kaufman, Social selection and peer influence in an online social network, Proc Natl Acad Sci U S A, 109 (2012), 68-72. doi: 10.1073/pnas.1109739109.

[143]

C. -T. Li, H. -P. Hsieh, S. -D. Lin and M. -K. Shan, Finding influential seed successors in social networks, in Proceedings of the 21st International Conference Companion on World Wide Web, ACM, Lyon, France, 2012,557–558. doi: 10.1145/2187980.2188125.

[144]

D. LiJ. TangY. DingX. ShuaiT. ChambersG. SunZ. Luo and J. Zhang, Topic-level opinion influence model (toim): An investigation using tencent microblogging, Journal of the Association for Information Science and Technology, 66 (2015), 2657-2673. doi: 10.1002/asi.23350.

[145]

G. LiS. ChenJ. FengK.-l. Tan and W.-s. Li, Efficient location-aware influence maximization, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2017), 87-98. doi: 10.1145/2588555.2588561.

[146]

H. LiS. S. BhowmickA. Sun and J. Cui, Conformity-aware influence maximization in online social networks, The VLDB Journal-The International Journal on Very Large Data Bases, 24 (2015), 117-141. doi: 10.1007/s00778-014-0366-x.

[147]

J. LiZ. CaiJ. WangM. Han and Y. Li, Truthful incentive mechanisms for geographical position conflicting mobile crowdsensing systems, IEEE Transactions on Computational Social Systems, 5 (2018), 324-334. doi: 10.1109/TCSS.2018.2797225.

[148]

J. LiX. GuoL. GuoS. JiM. Han and Z. Cai, Optimal routing with scheduling and channel assignment in multi-power multi-radio wireless sensor networks, Ad Hoc Networks, 31 (2015), 45-62. doi: 10.1016/j.adhoc.2015.03.006.

[149]

R.-H. LiL. QinJ. X. Yu and R. Mao, Influential community search in large networks, Proceedings of the VLDB Endowment, 8 (2015), 509-520. doi: 10.14778/2735479.2735484.

[150]

R. Li, S. Wang, H. Deng, R. Wang and K. C. -C. Chang, Towards social user profiling: Unified and discriminative influence model for inferring home locations, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, 1023–1031. doi: 10.1145/2339530.2339692.

[151]

Y. Li, W. Chen, Y. Wang and Z. -L. Zhang, Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships, in Proceedings of the sixth ACM international conference on Web search and data mining, ACM, Rome, Italy, 2013,657–666. doi: 10.1145/2433396.2433478.

[152]

Y. LiD. Zhang and K.-L. Tan, Real-time targeted influence maximization for online advertisements, Proceedings of the VLDB Endowment, 8 (2015), 1070-1081. doi: 10.14778/2794367.2794376.

[153]

S.-C. LinS.-D. Lin and M.-S. Chen, A learning-based framework to handle multi-round multi-party influence maximization on social networks, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 695-704. doi: 10.1145/2783258.2783392.

[154]

Y. Lin and J. Lui, Algorithmic design for competitive influence maximization problems, arXiv preprint, arXiv: 1410.8664.

[155]

X. Ling, C. Wu, S. Ji and M. Han, H2dos: An application-layer dos attack towards http/2 protocol, in Proceedings of SecureComm: Security and Privacy in Communication Networks 2017, SecureComm '17, 2017.

[156]

B. LiuG. CongY. ZengD. Xu and Y. M. Chee, Influence spreading path and its application to the time constrained social influence maximization problem and beyond, Knowledge and Data Engineering, IEEE Transactions on, 26 (2014), 1904-1917. doi: 10.1109/TKDE.2013.106.

[157]

L. Liu, J. Tang, J. Han, M. Jiang and S. Yang, Mining topic-level influence in heterogeneous networks, in Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM, Toronto, ON, Canada, 2010,199–208. doi: 10.1145/1871437.1871467.

[158]

L. LiuJ. TangJ. Han and S. Yang, Learning influence from heterogeneous social networks, Data Mining and Knowledge Discovery, 25 (2012), 511-544. doi: 10.1007/s10618-012-0252-3.

[159]

Q. LiuB. XiangE. ChenH. XiongF. Tang and J. X. Yu, Influence maximization over large-scale social networks: A bounded linear approach, CIKM '14 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, (2014), 171-180. doi: 10.1145/2661829.2662009.

[160]

X. Liu, M. Li, S. Li, S. Peng, X. Liao and X. Lu, Imgpu: Gpu accelerated influence maximization in large-scale social networks.

[161]

X. Liu, S. Li, X. Liao, L. Wang and Q. Wu, In-time estimation for influence maximization in large-scale social networks, in SNS '12 Proceedings of the Fifth Workshop on Social Network Systems, 2012, Article No. 3, 1–6. doi: 10.1145/2181176.2181179.

[162]

T. Lou and J. Tang, Mining structural hole spanners through information diffusion in social networks, 2013,825–836.

[163]

T. LouJ. TangJ. HopcroftZ. Fang and X. Ding, Learning to predict reciprocity and triadic closure in social networks, ACM Trans. Knowl. Discov. Data, 7 (2013), 1-25. doi: 10.1145/2499907.2499908.

[164]

J. -L. Lu, L. -Y. Wei and M. -Y. Yeh, Influence maximization in a social network in the presence of multiple influences and acceptances, 2014.

[165]

W. Lu, F. Bonchi, A. Goyal and L. V. S. Lakshmanan, The bang for the buck: Fair competitive viral marketing from the host perspective, in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Chicago, Illinois, USA, 2013,928–936. doi: 10.1145/2487575.2487649.

[166]

Y. Lu, J. Ren, J. Qian, M. Han, Y. Huo and T. Jing, Predictive contention window-based broadcast collision mitigation strategy for vanet, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,209–215. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.41.

[167]

Y. Lu, Y. Zhu, M. Han, J. S. He and Y. Zhang, A survey of gpu accelerated svm, in Proceedings of the 2014 ACM Southeast Regional Conference, ACM, 2014, Article No. 15. doi: 10.1145/2638404.2638474.

[168]

Z. LuL. FanW. WuB. Thuraisingham and K. Yang, Efficient influence spread estimation for influence maximization under the linear threshold model, Computational Social Networks, 1 (2014), 1-19. doi: 10.1186/s40649-014-0002-3.

[169]

B. LucierJ. Oren and Y. Singer, Influence at scale: Distributed computation of complex contagion in networks, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 735-744. doi: 10.1145/2783258.2783334.

[170]

Z. Z. M Han J Li, K-close: Algorithm for finding the close regions in wireless sensor networks based uncertain graph mining technology, Journal of Software, 22 (2011), 131-141.

[171]

K. Macropol and A. Singh, Scalable discovery of best clusters on large graphs, Proceedings of the VLDB Endowment, 3 (2010), 693-702. doi: 10.14778/1920841.1920930.

[172]

A. S. Maiya and T. Y. Berger-Wolf, Inferring the maximum likelihood hierarchy in social networks, in Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 4, IEEE Computer Society, 2009,245–250. doi: 10.1109/CSE.2009.235.

[173]

M. McPhersonL. Smith-Lovin and J. M. Cook, Birds of a feather: Homophily in social networks, Annual Review of Sociology, 27 (2001), 415-444. doi: 10.1146/annurev.soc.27.1.415.

[174]

I. Mele, F. Bonchi and A. Gionis, The early-adopter graph and its application to web-page recommendation, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, 2012, 1682–1686. doi: 10.1145/2396761.2398497.

[175]

S. MiharaS. Tsugawa and H. Ohsaki, Influence maximization problem for unknown social networks, ASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (2015), 1539-1546. doi: 10.1145/2808797.2808885.

[176]

S. Milgram, The small world problem, Psychology today, 2 (1967), 60-67.

[177]

A. Mislove, H. S. Koppula, K. P. Gummadi, P. Druschel and B. Bhattacharjee, Growth of the flickr social network, 2008, 25–30.

[178]

A. MisloveM. MarconK. P. GummadiP. Druschel and B. Bhattacharjee, Measurement and analysis of online social networks, IMC '07 Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, (2007), 29-42. doi: 10.1145/1298306.1298311.

[179]

E. Mossel and S. Roch, On the submodularity of influence in social networks, in Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, ACM, San Diego, California, USA, 2007,128–134. doi: 10.1145/1250790.1250811.

[180]

E. Mossel and G. Schoenebeck, Reaching consensus on social networks, 2010,214–229.

[181]

S. A. Myers and J. Leskovec, On the convexity of latent social network inference, threshold, 9 (2010), 20.

[182]

S. A. Myers and J. Leskovec, The bursty dynamics of the twitter information network, WWW '14 Proceedings of the 23rd International Conference on World Wide Web, (2014), 913-924. doi: 10.1145/2566486.2568043.

[183]

S. A. Myers, C. Zhu and J. Leskovec, Information diffusion and external influence in networks, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, 33–41. doi: 10.1145/2339530.2339540.

[184]

G. L. NemhauserL. A. Wolsey and M. L. Fisher, An analysis of approximations for maximizing submodular set functions. Ⅰ, Mathematical Programming, 14 (1978), 265-294. doi: 10.1007/BF01588971.

[185]

M. E. Newman, Spread of epidemic disease on networks, Physical Review E, 66 (2002), 016128, 11pp. doi: 10.1103/PhysRevE.66.016128.

[186]

M. E. Newman, The structure and function of complex networks, SIAM Review, 45 (2003), 167-256. doi: 10.1137/S003614450342480.

[187]

N. P. Nguyen, T. N. Dinh, X. Ying and M. T. Thai, Adaptive algorithms for detecting community structure in dynamic social networks, 2011 Proceedings IEEE INFOCOM, 2011. doi: 10.1109/INFCOM.2011.5935045.

[188]

N. P. Nguyen, G. Yan, M. T. Thai and S. Eidenbenz, Containment of misinformation spread in online social networks, in Proceedings of the 3rd Annual ACM Web Science Conference, ACM, Evanston, Illinois, 2012,213–222. doi: 10.1145/2380718.2380746.

[189]

J. Ok, Y. Jin, J. Choi, J. Shin and Y. Yi, Influence maximization over strategic diffusion in social networks, 2014 48th Annual Conference on Information Sciences and Systems (CISS), 2014. doi: 10.1109/CISS.2014.6814155.

[190]

J. P. Onnela and F. Reed-Tsochas, Spontaneous emergence of social influence in online systems, Proc Natl Acad Sci U S A, 107 (2010), 18375-18380. doi: 10.1073/pnas.0914572107.

[191]

L. Page, S. Brin, R. Motwani and T. Winograd, The pagerank citation ranking: Bringing order to the web.

[192]

W. PanW. DongM. CebrianT. KimJ. H. Fowler and A. S. Pentland, Modeling dynamical influence in human interaction: Using data to make better inferences about influence within social systems, Signal Processing Magazine, IEEE, 29 (2012), 77-86.

[193]

P. ParchasF. GulloD. Papadias and F. Bonchi, The pursuit of a good possible world: Extracting representative instances of uncertain graphs, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2014), 967-978. doi: 10.1145/2588555.2593668.

[194]

F. Paulsen, Tönnies, ferdinand. gemeinschaft und gesellschaft. abhandlung des communismus und des socialismus als empirischer culturformen. leipzig, fues's verlag, 1887, Vierteljahresschrift Für Wissenschaftliche Philosophie, 12 (1888), 111-119.

[195]

G. Ritzer and Others, The Blackwell Encyclopedia of Sociology vol. 1479, Blackwell Publishing Malden, MA, 2007.

[196]

M. G. Rodriguez, D. Balduzzi and B. Sch O Lkopf, Uncovering the temporal dynamics of diffusion networks, arXiv preprint, arXiv: 1105.0697.

[197]

M. G. Rodriguez, J. Leskovec and A. Krause, Inferring networks of diffusion and influence, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington, DC, USA, 2010, 1019–1028. doi: 10.1145/1835804.1835933.

[198]

M. G. Rodriguez and B. Sch O Lkopf, Influence maximization in continuous time diffusion networks, arXiv preprint, arXiv: 1205.1682.

[199]

D. M. Romero, W. Galuba, S. Asur and B. A. Huberman, Influence and passivity in social media, in Proceedings of the 20th International Conference Companion on World Wide Web, ACM, Hyderabad, India, 2011,113–114. doi: 10.1145/1963192.1963250.

[200]

Y. RongX. Wen and H. Cheng, A monte carlo algorithm for cold start recommendation, WWW '14 Proceedings of the 23rd International Conference on World Wide Web, (2014), 327-36. doi: 10.1145/2566486.2567978.

[201]

J. N. RosenquistJ. H. Fowler and N. A. Christakis, Social network determinants of depression, Molecular Psychiatry, 16 (2011), 273-281. doi: 10.1038/mp.2010.13.

[202]

R. A. Rossi, B. Gallagher, J. Neville and K. Henderson, Modeling dynamic behavior in large evolving graphs, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, Rome, Italy, 2013,667–676. doi: 10.1145/2433396.2433479.

[203]

M. Russell, Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, O'Reilly Media, 2011.

[204]

K. SaitoM. KimuraK. Ohara and H. Motoda, Efficient discovery of influential nodes for sis models in social networks, Knowledge and Information Systems, 30 (2012), 613-635. doi: 10.1007/s10115-011-0396-2.

[205]

K. Saito, M. Kimura, K. Ohara and H. Motoda, Learning asynchronous-time information diffusion models and its application to behavioral data analysis over social networks, Journal of Computer Engineering and Informatics, 1 (2013), 30–57, arXiv: 1204.4528. doi: 10.5963/JCEI0102002.

[206]

K. SaitoR. Nakano and M. Kimura, Prediction of information diffusion probabilities for independent cascade model, Knowledge-Based Intelligent Information and Engineering Systems, 5179 (2008), 67-75. doi: 10.1007/978-3-540-85567-5_9.

[207]

M. Salath EM. KazandjievaJ. W. LeeP. LevisM. W. Feldman and J. H. Jones, A high-resolution human contact network for infectious disease transmission, Proceedings of the National Academy of Sciences, 107 (2010), 22020-22025.

[208]

D. Sheldon, B. Dilkina, A. N. Elmachtoub, R. Finseth, A. Sabharwal, J. Conrad, C. P. Gomes, D. Shmoys, W. Allen, O. Amundsen and Others, Maximizing the spread of cascades using network design, arXiv preprint, arXiv: 1203.3514.

[209]

T. ShiS. ChengZ. CaiY. Li and J. Li, Retrieving the maximal time-bounded positive influence set from social networks, Personal and Ubiquitous Computing, 20 (2016), 717-730. doi: 10.1007/s00779-016-0943-7.

[210]

H. ShiokawaY. Fujiwara and M. Onizuka, Scan++: efficient algorithm for finding clusters, hubs and outliers on large-scale graphs, Proceedings of the VLDB Endowment, 8 (2015), 1178-1189. doi: 10.14778/2809974.2809980.

[211]

X. ShuaiY. DingJ. BusemeyerS. ChenY. Sun and J. Tang, Modeling indirect influence on twitter, International Journal on Semantic Web and Information Systems (IJSWIS), 8 (2012), 20-36. doi: 10.4018/jswis.2012100102.

[212]

Y. Singer, How to win friends and influence people, truthfully: Influence maximization mechanisms for social networks, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, Seattle, Washington, USA, 2012,733–742. doi: 10.4018/jswis.2012100102.

[213]

R. SiposA. Ghosh and T. Joachims, Was this review helpful to you?: It depends! context and voting patterns in online content, WWW '14 Proceedings of the 23rd International Conference on World Wide Web, (2014), 337-348. doi: 10.1145/2566486.2567998.

[214]

D. Song and D. A. Meyer, A model of consistent node types in signed directed social networks, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014. doi: 10.1109/ASONAM.2014.6921562.

[215]

G. SongX. ZhouY. Wang and K. Xie, Influence maximization on large-scale mobile social network: A divide-and-conquer method, Parallel and Distributed Systems, IEEE Transactions on, 26 (2015), 1379-1392. doi: 10.1109/TPDS.2014.2320515.

[216]

J. Stehl E, N. Voirin, A. Barrat, C. Cattuto, L. Isella, J. -F. C. C. O. Pinton, M. Quaggiotto, W. Van den Broeck, C. R E Gis, B. Lina and Others, High-resolution measurements of face-to-face contact patterns in a primary school, PloS one, 6 (2011), 23176.

[217]

J. Sun and J. Tang, A survey of models and algorithms for social influence analysis, in Social Network Data Analytics, Springer, 2011,177–214. doi: 10.1007/978-1-4419-8462-3_7.

[218]

T. Sun, W. Chen, Z. Liu, Y. Wang, X. Sun, M. Zhang and C. -Y. Lin, Participation maximization based on social influence in online discussion forums, 2011.

[219]

F. Tang, Q. Liu, H. Zhu, E. Chen and F. Zhu, Diversified social influence maximization, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014. doi: 10.1109/ASONAM.2014.6921625.

[220]

J. Tang, J. Sun, C. Wang and Z. Yang, Social influence analysis in large-scale networks, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Paris, France, 2009,807–816, 1557108. doi: 10.1145/1557019.1557108.

[221]

J. Tang, B. Wang, Y. Yang, P. Hu, Y. Zhao, X. Yan, B. Gao, M. Huang, P. Xu, W. Li and Others, Patentminer: topic-driven patent analysis and mining, KDD '12 Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, 1366–1374. doi: 10.1145/2339530.2339741.

[222]

J. Tang, S. Wu and J. Sun, Confluence: Conformity influence in large social networks, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois, USA, 2013,347–355. doi: 10.1145/2487575.2487691.

[223]

J. Tang, C. Zhang, K. Cai, L. Zhang and Z. Su, Sampling representative users from large social networks, 2015.

[224]

J. TangY. ZhangJ. SunJ. RaoW. YuY. Chen and A. C. M. Fong, Quantitative study of individual emotional states in social networks, Affective Computing, IEEE Transactions on, 3 (2012), 132-144.

[225]

X. Tang and C. C. Yang, Ranking user influence in healthcare social media, ACM Trans. Intell. Syst. Technol., 3 (2012), Article No. 73. doi: 10.1145/2337542.2337558.

[226]

Y. TangX. Xiao and Y. Shi, Influence maximization: Near-optimal time complexity meets practical efficiency, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2014), 75-86. doi: 10.1145/2588555.2593670.

[227]

J. TeevanD. Ramage and M. R. Morris, Twittersearch: A comparison of microblog search and web search, WSDM '11 Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, (2011), 35-44. doi: 10.1145/1935826.1935842.

[228]

G. Tong, W. Wu, S. Tang and D. -Z. Du, Adaptive influence maximization in dynamic social networks, IEEE/ACM Transactions on Networking, 25 (2017), 112–125, arXiv: 1506.06294. doi: 10.1109/TNET.2016.2563397.

[229]

W. TongR. Goebel and G. Lin, Smoothed heights of tries and patricia tries, Theoretical Computer Science, 609 (2016), 620-626. doi: 10.1016/j.tcs.2015.02.009.

[230]

H. Trottier and P. Philippe, Deterministic modeling of infectious diseases: theory and methods, The Internet Journal of Infectious Diseases, 1 (2001), 3.

[231]

J. Tsai, T. H. Nguyen and M. Tambe, Security games for controlling contagion, 2012.

[232]

W. VerbekeD. Martens and B. Baesens, Social network analysis for customer churn prediction, Applied Soft Computing, 14 (2014), 431-446. doi: 10.1016/j.asoc.2013.09.017.

[233]

J. ViderasA. L. OwenE. Conover and S. Wu, The influence of social relationships on pro-environment behaviors, Journal of Environmental Economics and Management, 63 (2012), 35-50. doi: 10.1016/j.jeem.2011.07.006.

[234]

B. ViswanathA. MisloveM. Cha and K. P. Gummadi, On the evolution of user interaction in facebook, WOSN '09 Proceedings of the 2nd ACM Workshop on Online Social Networks, (2009), 37-472. doi: 10.1145/1592665.1592675.

[235]

R. W O Lfer and H. Scheithauer, Social influence and bullying behavior: Intervention-based network dynamics of the fairplayer. manual bullying prevention program, Aggressive behavior.

[236]

C. WangW. Chen and Y. Wang, Scalable influence maximization for independent cascade model in large-scale social networks, Data Mining and Knowledge Discovery, 25 (2012), 545-576. doi: 10.1007/s10618-012-0262-1.

[237]

F. Wang, E. Camacho and K. Xu, Positive influence dominating set in online social networks, in Proceedings of the 3rd International Conference on Combinatorial Optimization and Applications, Springer-Verlag, Huangshan, China, 5573 (2009), 313–321. doi: 10.1007/978-3-642-02026-1_29.

[238]

G. Wang, Q. Hu and P. S. Yu, Influence and similarity on heterogeneous networks, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, 2012, 1462–1466. doi: 10.1145/2396761.2398453.

[239]

Y. Wang, G. Cong, G. Song and K. Xie, Community-based greedy algorithm for mining top-k influential nodes in mobile social networks, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington, DC, USA, 2010, 1039–1048. doi: 10.1145/1835804.1835935.

[240]

D. J. Watts and S. H. Strogatz, Collective dynamics of "small-world" networks, The Structure and Dynamics of Networks, (2011), 301-303. doi: 10.1515/9781400841356.301.

[241]

J. WengE. P. LimJ. Jiang and Q. He, Twitterrank: finding topic-sensitive influential twitterers, WSDM '10 Proceedings of the Third ACM International Conference on Web Search and Data Mining, (2010), 261-270. doi: 10.1145/1718487.1718520.

[242]

C. WilsonA. SalaK. P. N. Puttaswamy and B. Y. Zhao, Beyond social graphs: User interactions in online social networks and their implications, ACM Trans. Web, 6 (2012), 1-31. doi: 10.1145/2382616.2382620.

[243]

M. Workman, New media and the changing face of information technology use: The importance of task pursuit, social influence, and experience, Computers in Human Behavior, 31 (2014), 111-117. doi: 10.1016/j.chb.2013.10.008.

[244]

S. Wu, J. Sun and J. Tang, Patent partner recommendation in enterprise social networks, in Proceedings of the Sixth ACM International Conference on Web search and Data Mining, ACM, Rome, Italy, 2013, 43–52. doi: 10.1145/2433396.2433404.

[245]

X. XuN. YurukZ. Feng and T. A. J. Schweiger, Scan: a structural clustering algorithm for networks, KDD '07 Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2007), 824-833. doi: 10.1145/1281192.1281280.

[246]

M. Yan, M. Han, C. Ai, Z. Cai and Y. Li, Data aggregation scheduling in probabilistic wireless networks with cognitive radio capability, in IEEE GLOBECOM 2016, 2016. doi: 10.1109/GLOCOM.2016.7841716.

[247]

M. Yan, S. Ji, M. Han, Y. Li and Z. Cai, Data aggregation scheduling in wireless networks with cognitive radio capability, in Sensing, Communication, and Networking (SECON), 2014 Eleventh Annual IEEE International Conference on, IEEE, 2014,513–521.

[248]

D. -N. Yang, H. -J. Hung, W. -C. Lee and W. Chen, Maximizing acceptance probability for active friending in on-line social networks, arXiv preprint, arXiv: 1302.7025.

[249]

Y. Yang, J. Jia, S. Zhang, B. Wu, Q. Chen, J. Li, C. Xing and J. Tang, How do your friends on social media disclose your emotions?, 2014.

[250]

Y. Yang, J. Tang, C. Leung, Y. Sun, Q. Chen, J. Li and Q. Yang, Rain: Social role-aware information diffusion, 2015.

[251]

Z. YangJ. TangB. Xu and C. Xing, Active learning for networked data based on non-progressive diffusion model, WSDM '14 Proceedings of the 7th ACM International Conference on Web Search and Data Mining, (2014), 363-372. doi: 10.1145/2556195.2556223.

[252]

H. Yoganarasimhan, Impact of social network structure on content propagation: A study using youtube data, Quantitative Marketing and Economics, 10 (2012), 111-150. doi: 10.1007/s11129-011-9105-4.

[253]

H. Yu, S. -K. Kim and J. Kim, Scalable and parallelizable processing of influence maximization for large-scale social networks?, in Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), IEEE Computer Society, 2013,266–277.

[254]

X. YuX. RenY. SunB. SturtU. KhandelwalQ. GuB. Norick and J. Han, Recommendation in heterogeneous information networks with implicit user feedback, RecSys '13 Proceedings of the 7th ACM Conference on Recommender Systems, (2013), 347-350. doi: 10.1145/2507157.2507230.

[255]

Y. Yu, T. Y. Berger-Wolf, J. Saia and Others, Finding spread blockers in dynamic networks, in Advances in Social Network Mining and Analysis, Springer, 2010, 55–76.

[256]

H. Zhang, A. D. Procaccia and Y. Vorobeychik, Dynamic influence maximization under increasing returns to scale, 2015.

[257]

H. Zhang, T. N. Dinh and M. T. Thai, Maximizing the spread of positive influence in online social networks, 2013 IEEE 33rd International Conference on Distributed Computing Systems, 2013. doi: 10.1109/ICDCS.2013.37.

[258]

H. Zhang, S. Mishra and M. T. Thai, Recent advances in information diffusion and influence maximization of complex social networks, Opportunistic Mobile Social Networks, 37.

[259]

J. Zhang, B. Liu, J. Tang, T. Chen and J. Li, Social influence locality for modeling retweeting behaviors, in Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, AAAI Press, Beijing, China, 2013, 2761–2767.

[260]

J. Zhang, J. Tang, C. Ma, H. Tong, Y. Jing and J. Li, Panther: Fast top-k similarity search in large networks, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, 1445–1454, arXiv: 1504.02577. doi: 10.1145/2783258.2783267.

[261]

J. Zhang, J. Tang, H. Zhuang, C. W. -K. Leung and J. Li, Role-aware conformity influence modeling and analysis in social networks, 2014.

[262]

M. ZhangJ. TangX. Zhang and X. Xue, Addressing cold start in recommender systems: A semi-supervised co-training algorithm, SIGIR '14 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, (2014), 73-82. doi: 10.1145/2600428.2609599.

[263]

P. ZhangW. ChenX. SunY. Wang and J. Zhang, Minimizing seed set selection with probabilistic coverage guarantee in a social network, KDD '14 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), 1306-1315. doi: 10.1145/2623330.2623684.

[264]

J. ZhaoJ. WuX. FengH. Xiong and K. Xu, Information propagation in online social networks: A tie-strength perspective, Knowledge and Information Systems, 32 (2012), 589-608. doi: 10.1007/s10115-011-0445-x.

[265]

C. ZhouP. ZhangW. Zang and L. Guo, Maximizing the cumulative influence through a social network when repeat activation exists, Procedia Comput er Science, 29 (2014), 422-431. doi: 10.1016/j.procs.2014.05.038.

[266]

Y. Zhou and L. Liu, Social influence based clustering of heterogeneous information networks, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois, USA, 2013,338–346. doi: 10.1145/2487575.2487640.

[267]

H. Zhu, B. Huberman and Y. Luon, To switch or not to switch: Understanding social influence in online choices, in CHI '12, CHI '12, ACM, New York, NY, USA, 2012, 2257– 2266. doi: 10.1145/2207676.2208383.

[268]

H. ZhuangY. SunJ. TangJ. Zhang and X. Sun, Influence maximization in dynamic social networks, 2013 IEEE 13th International Conference on Data Mining, (2013), 1313-1318. doi: 10.1109/ICDM.2013.145.

show all references

References:
[1]

I. Abraham, S. Chechik, D. Kempe and A. Slivkins, Low-distortion inference of latent similarities from a multiplex social network, SIAM J. Comput., 44 (2015), 617–668, arXiv: 1202.0922. doi: 10.1137/130949191.

[2]

R. Agrawal, Nature of information, people, and relationships in digital social networks.

[3]

R. Agrawal, M. Potamias and E. Terzi, Learning the nature of information in social networks, 2012.

[4]

C. Ai, M. Han, J. Wang and M. Yan, An efficient social event invitation framework based on historical data of smart devices, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,229–236. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.44.

[5]

H. Albinali, M. Han, J. Wang, H. Gao and Y. Li, The roles of social network mavens, in The 12th International Conference on Mobile Ad-hoc and Sensor Networks (MSN 2016), 2016, 1–12. doi: 10.1109/MSN.2016.009.

[6]

A. Anagnostopoulos, R. Kumar and M. Mahdian, Influence and correlation in social networks, in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Las Vegas, Nevada, USA, 2008, 7–15. doi: 10.1145/1401890.1401897.

[7]

C. AnagnostopoulosS. Hadjiefthymiades and E. Zervas, An analytical model for multi-epidemic information dissemination, J. Parallel Distrib. Comput., 71 (2011), 87-104, 1891295. doi: 10.1016/j.jpdc.2010.08.010.

[8]

T. C. AntonucciK. J. Ajrouch and K. S. Birditt, The convoy model: Explaining social relations from a multidisciplinary perspective, The Gerontologist, 54 (2014), 82-92. doi: 10.1093/geront/gnt118.

[9]

S. E. Asch, Opinions and social pressure, Readings about the social animal, 193 (1955), 17-26. doi: 10.1038/scientificamerican1155-31.

[10]

C. C. I. AslayW. LuF. BonchiA. Goyal and L. V. S. Lakshmanan, Viral marketing meets social advertising: Ad allocation with minimum regret, Proceedings of the VLDB Endowment VLDB Endowment Hompage Archive, 8 (2015), 814-825. doi: 10.14778/2752939.2752950.

[11]

D. B. BahrR. C. BrowningH. R. Wyatt and J. O. Hill, Exploiting social networks to mitigate the obesity epidemic, Obesity (Silver Spring), 17 (2009), 723-728. doi: 10.1038/oby.2008.615.

[12]

E. Bakshy, D. Eckles, R. Yan and I. Rosenn, Social influence in social advertising: Evidence from field experiments, in Proceedings of the 13th ACM Conference on Electronic Commerce, ACM, Valencia, Spain, 2012,146–161. doi: 10.1145/2229012.2229027.

[13]

E. Bakshy, J. M. Hofman, W. A. Mason and D. J. Watts, Everyone's an influencer: quantifying influence on twitter, in Proceedings of the fourth ACM international conference on Web search and data mining, ACM, Hong Kong, China, 2011, 65–74. doi: 10.1145/1935826.1935845.

[14]

E. Bakshy, I. Rosenn, C. Marlow and L. Adamic, The role of social networks in information diffusion, in Proceedings of the 21st International Conference on World Wide Web, ACM, Lyon, France, 2012,519–528. doi: 10.1145/2187836.2187907.

[15]

N. Barbieri and F. Bonchi, Influence maximization with viral product design, Proceedings of the 2014 SIAM International Conference on Data Mining, 2014, p9. doi: 10.1137/1.9781611973440.7.

[16]

N. Barbieri, F. Bonchi and G. Manco, Topic-aware social influence propagation models, in Proceedings of the 2012 IEEE 12th International Conference on Data Mining, IEEE Computer Society, 2012, 81–90.

[17]

S. Bhagat, A. Goyal and L. V. S. Lakshmanan, Maximizing product adoption in social networks, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, Seattle, Washington, USA, 2012,603–612. doi: 10.1145/2124295.2124368.

[18]

S. Bharathi, D. Kempe and M. Salek, Competitive influence maximization in social networks, in Internet and Network Economics, Springer, 2007,306–311. doi: 10.1007/978-3-540-77105-0_31.

[19]

K. Bhawalkar, S. Gollapudi and K. Munagala, Coevolutionary opinion formation games, STOC'13Proceedings of the 2013 ACM Symposium on Theory of Computing, 41–50, ACM, New York, 2013. doi: 10.1145/2488608.2488615.

[20]

F. Bonchi, Influence propagation in social networks: A data mining perspective, IEEE Intelligent Informatics Bulletin, 12 (2011), 8-16. doi: 10.1109/WI-IAT.2011.292.

[21]

C. Borgs, M. Brautbar, J. Chayes and B. Lucier, Maximizing social influence in nearly optimal time, Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 946–957, ACM, New York, 2014. doi: 10.1137/1.9781611973402.70.

[22]

A. Borodin, Y. Filmus and J. Oren, Threshold models for competitive influence in social networks, in Proceedings of the 6th international conference on Internet and network economics, Springer-Verlag, Stanford, CA, USA, 2010,539–550. doi: 10.1007/978-3-642-17572-5_48.

[23]

S. BourigaultC. LagnierS. LamprierL. Denoyer and P. Gallinari, Learning social network embeddings for predicting information diffusion, WSDM '14 Proceedings of the 7th ACM International Conference on Web Search and Data Mining, (2014), 393-402. doi: 10.1145/2556195.2556216.

[24]

C. Budak and R. Agrawal, On participation in group chats on twitter, 2013,165–176.

[25]

J. T. Cacioppo, J. H. Fowler and N. A. Christakis, Alone in the crowd: the structure and spread of loneliness in a large social network., Journal of Personality and Social Psychology, 97 (2009), 977.

[26]

J. L. Z. Cai, M. Yan and Y. Li, Using crowdsourced data in location-based social networks to explore influence maximization, in Computer Communications, IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on, IEEE, 2016, 1–9. doi: 10.1109/INFOCOM.2016.7524471.

[27]

Z. Cai, Z. He, X. Guan and Y. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, (2016), p1. doi: 10.1109/TDSC.2016.2613521.

[28]

J. Cannarella and J. A. Spechler, Epidemiological modeling of online social network dynamics, arXiv preprint, arXiv: 1401.4208.

[29]

T. CarnesC. NagarajanS. M. Wild and A. Van Zuylen, Maximizing influence in a competitive social network: a follower's perspective, ICEC '07 Proceedings of the Ninth International Conference on Electronic Commerce, (2007), 351-360. doi: 10.1145/1282100.1282167.

[30]

M. ChaH. HaddadiF. Benevenuto and P. K. Gummadi, Measuring user influence in twitter: The million follower fallacy, ICWSM, 10 (2010), 10-17.

[31]

M. Cha, A. Mislove and K. P. Gummadi, A measurement-driven analysis of information propagation in the flickr social network, 2009,721–730.

[32]

M. Cha, A. Mislove and K. P. Gummadi, A measurement-driven analysis of information propagation in the flickr social network, in Proceedings of the 18th International Conference on World Wide Web, ACM, Madrid, Spain, 2009,721–730. doi: 10.1145/1526709.1526806.

[33]

Y. Chang, X. Wang, Q. Mei and Y. Liu, Towards twitter context summarization with user influence models, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, Rome, Italy, 2013,527–536. doi: 10.1145/2433396.2433464.

[34]

V. Chaoji, S. Ranu, R. Rastogi and R. Bhatt, Recommendations to boost content spread in social networks, in Proceedings of the 21st International Conference on World Wide Web, ACM, Lyon, France, 2012,529–538. doi: 10.1145/2187836.2187908.

[35]

L. Chen, X. Li and J. Han, Medrank: discovering influential medical treatments from literature by information network analysis, in Proceedings of the Twenty-Fourth Australasian Database Conference, Australian Computer Society, Inc., Adelaide, Australia, 2013, 3–12.

[36]

S. ChenJ. FanG. LiJ. FengK.-l. Tan and J. Tang, Online topic-aware influence maximization, Proceedings of the VLDB Endowment, 8 (2015), 666-677. doi: 10.14778/2735703.2735706.

[37]

W. ChenA. CollinsR. CummingsT. KeZ. LiuD. RinconX. SunY. WangW. Wei and Y. Yuan, Influence maximization in social networks when negative opinions may emerge and propagate, Proceedings of the 2011 SIAM International Conference on Data Mining, (2011), 379-390. doi: 10.1137/1.9781611972818.33.

[38]

W. Chen, T. Lin and C. Yang, Efficient topic-aware influence maximization using preprocessing, CoRR, abs/1403.0057.

[39]

W. ChenZ. LiuX. Sun and Y. Wang, A game-theoretic framework to identify overlapping communities in social networks, Data Min. Knowl. Discov., 21 (2010), 224-240. doi: 10.1007/s10618-010-0186-6.

[40]

W. Chen, P. Lu, X. Sun, B. Tang, Y. Wang and Z. A. Zhu, Optimal pricing in social networks with incomplete information, in Internet and Network Economics, Springer, 2011, 49–60.

[41]

W. Chen, W. Lu and N. Zhang, Time-critical influence maximization in social networks with time-delayed diffusion process, 2012.

[42]

W. ChenC. Wang and Y. Wang, Scalable influence maximization for prevalent viral marketing in large-scale social networks, Data Min. Knowl. Discov., 25 (2012), 545-576. doi: 10.1007/s10618-012-0262-1.

[43]

W. Chen, Y. Wang and S. Yang, Efficient influence maximization in social networks, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Paris, France, 2009,199–208. doi: 10.1145/1557019.1557047.

[44]

W. Chen, Y. Yuan and L. Zhang, Scalable influence maximization in social networks under the linear threshold model, in Proceedings of the 2010 IEEE International Conference on Data Mining, IEEE Computer Society, 2010, 88–97. doi: 10.1109/ICDM.2010.118.

[45]

Y. -C. Chen, W. -Y. Zhu, W. -C. Peng, W. -C. Lee and S. -Y. Lee, Cim: community-based influence maximization in social networks, ACM Transactions on Intelligent Systems and Technology (TIST), 5 (2014), Article No. 25. doi: 10.1145/2532549.

[46]

S. ChengH. ShenJ. HuangW. Chen and X. Cheng, Imrank: Influence maximization via finding self-consistent ranking, SIGIR '14 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, (2014), 475-484. doi: 10.1145/2600428.2609592.

[47]

N. A. Christakis and J. H. Fowler, The spread of obesity in a large social network over 32 years, N Engl J Med, 357 (2007), 370-379. doi: 10.1056/NEJMsa066082.

[48]

N. A. Christakis and J. H. Fowler, The collective dynamics of smoking in a large social network, New England Journal of Medicine, 358 (2008), 2249-2258. doi: 10.1056/NEJMsa0706154.

[49]

P. Clifford and A. Sudbury, A model for spatial conflict, Biometrika, 60 (1973), 581-588. doi: 10.1093/biomet/60.3.581.

[50]

L. CorazziniF. PavesiB. Petrovich and L. Stanca, Influential listeners: An experiment on persuasion bias in social networks, European Economic Review, 56 (2012), 1276-1288.

[51]

D. Cosley, D. P. Huttenlocher, J. M. Kleinberg, X. Lan and S. Suri, Sequential influence models in social networks., ICWSM, 10 (2010), 26.

[52]

D. M. Cutler and E. L. Glaeser, Social interactions and smoking, Technical report, National Bureau of Economic Research, (2007), 1-28. doi: 10.3386/w13477.

[53]

A. DasS. Gollapudi and K. Munagala, Modeling opinion dynamics in social networks, WSDM '14 Proceedings of the 7th ACM International Conference on Web Search and Data Mining, (2014), 403-412. doi: 10.1145/2556195.2559896.

[54]

A. Das, S. Gollapudi, R. Panigrahy and M. Salek, Debiasing social wisdom, 2013,500–508.

[55]

A. Datta, A. Datta, A. D. Procaccia and Y. Zick, Influence in classification via cooperative game theory, arXiv preprint, arXiv: 1505.00036.

[56]

I. de Sola Pool and M. Kochen, Contacts and influence, Social Networks, 1 (1979), 5-51. doi: 10.1016/0378-8733(78)90011-4.

[57]

E. D. Demaine, M. Hajiaghayi, H. Mahini, D. L. Malec, S. Raghavan, A. Sawant and M. Zadimoghadam, How to influence people with partial incentives, in World Wide Web Conferences, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 2014,937–948. doi: 10.1145/2566486.2568039.

[58]

T. N. Dinh, D. T. Nguyen and M. T. Thai, Cheap, easy, and massively effective viral marketing in social networks: truth or fiction?, in Proceedings of the 23rd ACM conference on Hypertext and Social Media, ACM, Milwaukee, Wisconsin, USA, 2012,165–174. doi: 10.1145/2309996.2310024.

[59]

P. S. DoddsR. Muhamad and D. J. Watts, An experimental study of search in global social networks, Science, 301 (2003), 827-829. doi: 10.1126/science.1081058.

[60]

P. Domingos and M. Richardson, Mining the network value of customers, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Francisco, California, 2001, 57–66. doi: 10.1145/502512.502525.

[61]

Y. DongR. A. Johnson and N. V. Chawla, Will this paper increase your h-index?: Scientific impact prediction, Machine Learning and Knowledge Discovery in Databases, (2015), 259-263. doi: 10.1007/978-3-319-23461-8_26.

[62]

Z. Duan, W. Li and Z. Cai, Distributed auctions for task assignment and scheduling in mobile crowdsensing systems, in Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on, IEEE, 2017,635–644. doi: 10.1109/ICDCS.2017.121.

[63]

Z. Duan, M. Yan, Z. Cai, X. Wang, M. Han and Y. Li, Truthful incentive mechanisms for social cost minimization in mobile crowdsourcing systems, Sensors, 16 (2016), 481. doi: 10.3390/s16040481.

[64]

I. Eleta, Multilingual use of twitter: Social networks and language choice, in ACM Conference on Computer-Supported Cooperative Work and Social Computing, ACM, New York, NY, USA, 2012,363–366. doi: 10.1145/2141512.2141621.

[65]

E. Even-Dar and A. Shapira, A note on maximizing the spread of influence in social networks, in Internet and Network Economics, Springer, 2007,281–286. doi: 10.1007/978-3-540-77105-0_27.

[66]

Y. Fan and C. R. Shelton, Learning continuous-time social network dynamics, 2009,161–168.

[67]

K. FengG. CongS. S. Bhowmick and S. Ma, In search of influential event organizers in online social networks, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2014), 63-74. doi: 10.1145/2588555.2612173.

[68]

J. H. Fowler, N. A. Christakis, Steptoe and D. Roux, Dynamic spread of happiness in a large social network: Longitudinal analysis of the framingham heart study social network, BMJ: British Medical Journal, 23–27.

[69]

L. C. Freeman, A set of measures of centrality based on betweenness, Sociometry, 40 (1977), 35-41. doi: 10.2307/3033543.

[70]

P. J. GiabbanelliA. AlimadadV. Dabbaghian and D. T. Finegood, Modeling the influence of social networks and environment on energy balance and obesity, Journal of Computational Science, 3 (2012), 17-27.

[71]

A. Goyal, F. Bonchi and L. V. S. Lakshmanan, Learning influence probabilities in social networks, in Proceedings of the Third ACM International Conference on Web Search and Data Mining, ACM, New York, New York, USA, 2010,241–250. doi: 10.1145/1718487.1718518.

[72]

A. GoyalF. Bonchi and L. V. S. Lakshmanan, A data-based approach to social influence maximization, Proc. VLDB Endow., 5 (2011), 73-84. doi: 10.14778/2047485.2047492.

[73]

A. Goyal, F. Bonchi, L. V. Lakshmanan and S. Venkatasubramanian, Approximation analysis of influence spread in social networks, arXiv preprint, arXiv: 1008.2005.

[74]

A. GoyalF. BonchiL. V. Lakshmanan and S. Venkatasubramanian, On minimizing budget and time in influence propagation over social networks, Social Network Analysis and Mining, 3 (2013), 179-192. doi: 10.1007/s13278-012-0062-z.

[75]

A. Goyal and L. V. S. Lakshmanan, Recmax: Exploiting recommender systems for fun and profit, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, 1294–1302. doi: 10.1145/2339530.2339731.

[76]

A. Goyal, W. Lu and L. V. S. Lakshmanan, Celf++: Optimizing the greedy algorithm for influence maximization in social networks, in Proceedings of the 20th International Conference Companion on World Wide Web, Proceedings of the 20th international conference companion on World wide web, ACM, Hyderabad, India, 2011, 47–48. doi: 10.1145/1963192.1963217.

[77]

A. Goyal, W. Lu and L. V. S. Lakshmanan, Simpath: An efficient algorithm for influence maximization under the linear threshold model, in Proceedings of the 2011 IEEE 11th International Conference on Data Mining, IEEE Computer Society, 2011,211–220. doi: 10.1109/ICDM.2011.132.

[78]

S. Goyal and M. Kearns, Competitive contagion in networks, STOC'12Proceedings of the 2012 ACM Symposium on Theory of Computing, (2012), 759-774. doi: 10.1145/2213977.2214046.

[79]

M. Grabisch and A. Rusinowska, A model of influence in a social network, Theory and Decision, 69 (2010), 69-96. doi: 10.1007/s11238-008-9109-z.

[80]

M. Granovetter, The strength of weak ties, American Journal of Sociology, 78 (1973), l.

[81]

D. Gruhl, R. Guha, D. Liben-Nowell and A. Tomkins, Information diffusion through blogspace, 2004,491–501.

[82]

A. GuilleH. HacidC. E. C. Favre and D. A. Zighed, Information diffusion in online social networks: A survey, ACM SIGMOD Record, 42 (2013), 17-28. doi: 10.1145/2503792.2503797.

[83]

B. HanP. HuiV. A. KumarM. V. MaratheJ. Shao and A. Srinivasan, Mobile data offloading through opportunistic communications and social participation, Mobile Computing, IEEE Transactions on, 11 (2012), 821-834. doi: 10.1109/TMC.2011.101.

[84]

B. Han and A. Srinivasan, Your friends have more friends than you do: identifying influential mobile users through random walks, in Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing, ACM, Hilton Head, South Carolina, USA, 2012, 5–14. doi: 10.1145/2248371.2248376.

[85]

M. HanZ. DuanC. AiF. W. LybargerY. Li and A. G. Bourgeois, Time constraint influence maximization algorithm in the age of big data, International Journal of Computational Science and Engineering, 15 (2017), 165-175. doi: 10.1504/IJCSE.2017.087401.

[86]

M. Han, Z. Duan and Y. Li, Privacy issues for transportation cyber physical systems, in Secure and Trustworthy Transportation Cyber-Physical Systems, Springer, Singapore, 2017, 67–86. doi: 10.1007/978-981-10-3892-1_4.

[87]

M. HanQ. HanL. LiJ. Li and Y. Li, Maximizing influence in sensed heterogenous social network with privacy preservation, International Journal of Sensor Networks, (2017), 1-11. doi: 10.1504/IJSNET.2017.10007412.

[88]

M. Han, J. Li, Z. Cai and Q. Han, Privacy reserved influence maximization in gps-enabled cyber-physical and online social networks, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,284–292. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.51.

[89]

M. HanJ. Li and Z. Zou, Finding k close subgraphs in an uncertain graph, Jisuanji Kexue yu Tansuo, 5 (2011), 791-803.

[90]

M. HanL. LiX. PengZ. Hong and M. Li, Information privacy of cyber transportation system: Opportunities and challenges, RIIT '17 Proceedings of the 6th Annual Conference on Research in Information Technology, (2017), 23-28. doi: 10.1145/3125649.3125652.

[91]

M. HanL. LiY. XieJ. WangZ. DuanJ. Li and M. Yan, Cognitive approach for location privacy protection, IEEE Access, 6 (2018), 13466-13477. doi: 10.1109/ACCESS.2018.2805464.

[92]

M. Han, Y. Liang, Z. Duan and Y. Wang, Mining public business knowledge: A case study in sec's edgar, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,393–400. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.65.

[93]

M. HanJ. WangM. YanC. AiZ. Duan and Z. Hong, Near-complete privacy protection: Cognitive optimal strategy in location-based services, Procedia Computer Science, 129 (2018), 298-304. doi: 10.1016/j.procs.2018.03.079.

[94]

M. HanM. YanZ. Cai and Y. Li, An exploration of broader influence maximization in timeliness networks with opportunistic selection, Journal of Network and Computer Applications, 63 (2016), 39-49. doi: 10.1016/j.jnca.2016.01.004.

[95]

M. Han, M. Yan, Z. Cai, Y. Li, X. Cai and J. Yu, Influence maximization by probing partial communities in dynamic online social networks, Transactions on Emerging Telecommunications Technologies, 28 (2017), e3054. doi: 10.1002/ett.3054.

[96]

M. Han, M. Yan, J. Li, S. Ji and Y. Li, Generating uncertain networks based on historical network snapshots, in COCOON, 2013,747–758. doi: 10.1007/978-3-642-38768-5_68.

[97]

M. HanM. YanJ. LiS. Ji and Y. Li, Neighborhood-based uncertainty generation in social networks, Journal of Combinatorial Optimization, 28 (2014), 561-576. doi: 10.1007/s10878-013-9684-y.

[98]

M. HanW. Zhang and J.-Z. Li, Raking: An efficient k-maximal frequent pattern mining algorithm on uncertain graph database, Jisuanji Xuebao(Chinese Journal of Computers), 33 (2010), 1387-1395. doi: 10.3724/SP.J.1016.2010.01387.

[99]

R. A. Hanneman and M. Riddle, Introduction to social network methods, 2005.

[100]

D. Hatano, T. Fukunaga, T. Maehara and K. -i. Kawarabayashi, Lagrangian decomposition algorithm for allocating marketing channels, 2015.

[101]

J. He, J. Hopcroft, H. Liang, S. Suwajanakorn and L. Wang, Detecting the structure of social networks using (α, β)-communities, in Algorithms and Models for the Web Graph, Springer, 6732 (2011), 26–37. doi: 10.1007/978-3-642-21286-4_3.

[102]

X. He and D. Kempe, Price of anarchy for the n-player competitive cascade game with submodular activation functions, in Web and Internet Economics, Springer, 2013,232–248. doi: 10.1007/978-3-642-45046-4_20.

[103]

X. He and D. Kempe, Stability of influence maximization, KDD '14 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), 1256-1265. doi: 10.1145/2623330.2623746.

[104]

X. He, G. Song, W. Chen and Q. Jiang, Influence blocking maximization in social networks under the competitive linear threshold model, 2012,463–474.

[105]

Z. He, Z. Cai and X. Wang, Modeling propagation dynamics and developing optimized countermeasures for rumor spreading in online social networks, in Distributed Computing Systems (ICDCS), 2015 IEEE 35th International Conference on, IEEE, 2015,205–214. doi: 10.1109/ICDCS.2015.29.

[106]

Z. HeZ. CaiJ. YuX. WangY. Sun and Y. Li, Cost-efficient strategies for restraining rumor spreading in mobile social networks, IEEE Transactions on Vehicular Technology, 66 (2017), 2789-2800. doi: 10.1109/TVT.2016.2585591.

[107]

M. HeidariM. Asadpour and H. Faili, Smg: Fast scalable greedy algorithm for influence maximization in social networks, Physica A: Statistical Mechanics and its Applications, 420 (2015), 124-133. doi: 10.1016/j.physa.2014.10.088.

[108]

C. Hoede and R. R. Bakker, A theory of decisional power, Journal of Mathematical Sociology, 8 (1982), 309-322. doi: 10.1080/0022250X.1982.9989927.

[109]

J. HopcroftT. Lou and J. Tang, Who will follow you back?: Reciprocal relationship prediction, CIKM '11 Proceedings of the 20th ACM International Conference on Information and Knowledge Management, (2011), 1137-1146. doi: 10.1145/2063576.2063740.

[110]

J. HuK. MengX. ChenC. Lin and J. Huang, Analysis of influence maximization in large-scale social networks, SIGMETRICS Perform. Eval. Rev., 41 (2014), 78-81. doi: 10.1145/2627534.2627559.

[111]

X. Hu, L. Tang, J. Tang and H. Liu, Exploiting social relations for sentiment analysis in microblogging, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, Rome, Italy, 2013,537–546. doi: 10.1145/2433396.2433465.

[112]

Z. HuJ. YaoB. Cui and E. Xing, Community level diffusion extraction, SIGMOD '15 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, (2015), 1555-1569. doi: 10.1145/2723372.2723737.

[113]

H. Huang, J. Tang, S. Wu, L. Liu and Others, Mining triadic closure patterns in social networks, WWW '14 Companion Proceedings of the 23rd International Conference on World Wide Web, 2014,499–504. doi: 10.1145/2567948.2576940.

[114]

J. -P. Huang, C. -Y. Wang and H. -Y. Wei, Strategic information diffusion through online social networks, in Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ACM, Barcelona, Spain, 2011, Article No. 88, 5pp. doi: 10.1145/2093698.2093786.

[115]

J. Huang, X. -Q. Cheng, H. -W. Shen, T. Zhou and X. Jin, Exploring social influence via posterior effect of word-of-mouth recommendations, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, Seattle, Washington, USA, 2012,573–582. doi: 10.1145/2124295.2124365.

[116]

J. E. L. Iribarren and E. Moro, Impact of human activity patterns on the dynamics of information diffusion, Physical Review Letters, 103 (2009), 038702. doi: 10.1103/PhysRevLett.103.038702.

[117]

J. H. JanssenW. A. IJsselsteijn and J. H. Westerink, How affective technologies can influence intimate interactions and improve social connectedness, International Journal of Human-Computer Studies, 72 (2014), 33-43. doi: 10.1016/j.ijhcs.2013.09.007.

[118]

S. Ji, Z. Cai, M. Han and R. Beyah, Whitespace measurement and virtual backbone construction for cognitive radio networks: From the social perspective, in Sensing, Communication, and Networking (SECON), 2015 12th Annual IEEE International Conference on, IEEE, 2015,435–443. doi: 10.1109/SAHCN.2015.7338344.

[119]

F. Jiang, S. Jin, Y. Wu and J. Xu, A uniform framework for community detection via influence maximization in social networks, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014. doi: 10.1109/ASONAM.2014.6921556.

[120]

A. P. JoshiM. Han and Y. Wang, A survey on security and privacy issues of blockchain technology, Mathematical Foundations of Computing, 1 (2018), 121-147.

[121]

K. Jung, W. Heo and W. Chen, Irie: A scalable influence maximization algorithm for independent cascade model and its extensions, arXiv preprint, arXiv: 1111.4795.

[122]

D. Kempe, J. Kleinberg, S. Oren and A. Slivkins, Selection and influence in cultural dynamics, in Proceedings of the fourteenth ACM conference on Electronic commerce, ACM, Philadelphia, Pennsylvania, USA, 2013,585–586. doi: 10.1145/2492002.2482566.

[123]

D. KempeJ. Kleinberg and E. V. Tardos, Influential nodes in a diffusion model for social networks, Automata, Languages and Programming, (2005), 1127-1138. doi: 10.1007/11523468_91.

[124]

D. KempeJ. Kleinberg and V. Tardos, Maximizing the spread of influence through a social network, Theory Comput., 11 (2015), 105-147. doi: 10.4086/toc.2015.v011a004.

[125]

S. Khanna and B. Lucier, Influence maximization in undirected networks, Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, 1482–1496, ACM, New York, 2014. doi: 10.1137/1.9781611973402.109.

[126]

Y. A. Kim and J. Srivastava, Impact of social influence in e-commerce decision making, in Proceedings of the Ninth International Conference on Electronic Commerce, ACM, Minneapolis, MN, USA, 2007,293–302. doi: 10.1145/1282100.1282157.

[127]

M. KimuraK. SaitoR. Nakano and H. Motoda, Extracting influential nodes on a social network for information diffusion, Data Min. Knowl. Discov., 20 (2010), 70-97. doi: 10.1007/s10618-009-0150-5.

[128]

F. Kooti, W. A. Mason, K. P. Gummadi and M. Cha, Predicting emerging social conventions in online social networks, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, 2012,445–454, 2396820. doi: 10.1145/2396761.2396820.

[129]

J. Kostka, Y. A. Oswald and R. Wattenhofer, Word of mouth: Rumor dissemination in social networks, in Structural Information and Communication Complexity, Springer, 5058 (2008), 185–196. doi: 10.1007/978-3-540-69355-0_16.

[130]

R. KumarJ. NovakP. Raghavan and A. Tomkins, On the bursty evolution of blogspace, WWW '03 Proceedings of the 12th international conference on World Wide Web, (2003), 568-576. doi: 10.1145/775152.775233.

[131]

R. KumarJ. Novak and A. Tomkins, Structure and evolution of online social networks, KDD '06 Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2006), 611-617. doi: 10.1145/1150402.1150476.

[132]

O. Kwon and Y. Wen, An empirical study of the factors affecting social network service use, Computers in Human Behavior, 26 (2010), 254-263. doi: 10.1016/j.chb.2009.04.011.

[133]

T. La Fond and J. Neville, Randomization tests for distinguishing social influence and homophily effects, WWW '10 Proceedings of the 19th International Conference on World Wide Web, (2010), 601-610. doi: 10.1145/1772690.1772752.

[134]

M. Lahiri, A. S. Maiya, R. Sulo, Habiba and T. Y. B. Wolf, The impact of structural changes on predictions of diffusion in networks, in Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, IEEE Computer Society, 2008,939–948. doi: 10.1109/ICDMW.2008.92.

[135]

X. N. LamT. VuT. D. Le and A. D. Duong, Addressing cold-start problem in recommendation systems, CUIMC '08 Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, (2008), 208-211. doi: 10.1145/1352793.1352837.

[136]

I. Leftheriotis and M. N. Giannakos, Using social media for work: Losing your time or improving your work?, Computers in Human Behavior, 31 (2014), 134-142. doi: 10.1016/j.chb.2013.10.016.

[137]

S. LeiS. ManiuL. MoR. Cheng and P. Senellart, Online influence maximization, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 645-654. doi: 10.1145/2783258.2783271.

[138]

J. LeskovecL. Backstrom and J. Kleinberg, Meme-tracking and the dynamics of the news cycle, KDD '09 Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2009), 497-506. doi: 10.1145/1557019.1557077.

[139]

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen and N. Glance, Costeffective outbreak detection in networks, in Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, San Jose, California, USA, 2007,420–429. doi: 10.1145/1281192.1281239.

[140]

J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance and M. Hurst, Information propagation and network evolution on the web, DA Project, Machine Learning Dept. Carnegie Mellon University.

[141]

J. LeskovecM. McGlohonC. FaloutsosN. S. Glance and M. Hurst, Patterns of cascading behavior in large blog graphs, Proceedings of the 2007 SIAM International Conference on Data Miningvol, 7 (2007), 551-556. doi: 10.1137/1.9781611972771.60.

[142]

K. LewisM. Gonzalez and J. Kaufman, Social selection and peer influence in an online social network, Proc Natl Acad Sci U S A, 109 (2012), 68-72. doi: 10.1073/pnas.1109739109.

[143]

C. -T. Li, H. -P. Hsieh, S. -D. Lin and M. -K. Shan, Finding influential seed successors in social networks, in Proceedings of the 21st International Conference Companion on World Wide Web, ACM, Lyon, France, 2012,557–558. doi: 10.1145/2187980.2188125.

[144]

D. LiJ. TangY. DingX. ShuaiT. ChambersG. SunZ. Luo and J. Zhang, Topic-level opinion influence model (toim): An investigation using tencent microblogging, Journal of the Association for Information Science and Technology, 66 (2015), 2657-2673. doi: 10.1002/asi.23350.

[145]

G. LiS. ChenJ. FengK.-l. Tan and W.-s. Li, Efficient location-aware influence maximization, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2017), 87-98. doi: 10.1145/2588555.2588561.

[146]

H. LiS. S. BhowmickA. Sun and J. Cui, Conformity-aware influence maximization in online social networks, The VLDB Journal-The International Journal on Very Large Data Bases, 24 (2015), 117-141. doi: 10.1007/s00778-014-0366-x.

[147]

J. LiZ. CaiJ. WangM. Han and Y. Li, Truthful incentive mechanisms for geographical position conflicting mobile crowdsensing systems, IEEE Transactions on Computational Social Systems, 5 (2018), 324-334. doi: 10.1109/TCSS.2018.2797225.

[148]

J. LiX. GuoL. GuoS. JiM. Han and Z. Cai, Optimal routing with scheduling and channel assignment in multi-power multi-radio wireless sensor networks, Ad Hoc Networks, 31 (2015), 45-62. doi: 10.1016/j.adhoc.2015.03.006.

[149]

R.-H. LiL. QinJ. X. Yu and R. Mao, Influential community search in large networks, Proceedings of the VLDB Endowment, 8 (2015), 509-520. doi: 10.14778/2735479.2735484.

[150]

R. Li, S. Wang, H. Deng, R. Wang and K. C. -C. Chang, Towards social user profiling: Unified and discriminative influence model for inferring home locations, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, 1023–1031. doi: 10.1145/2339530.2339692.

[151]

Y. Li, W. Chen, Y. Wang and Z. -L. Zhang, Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships, in Proceedings of the sixth ACM international conference on Web search and data mining, ACM, Rome, Italy, 2013,657–666. doi: 10.1145/2433396.2433478.

[152]

Y. LiD. Zhang and K.-L. Tan, Real-time targeted influence maximization for online advertisements, Proceedings of the VLDB Endowment, 8 (2015), 1070-1081. doi: 10.14778/2794367.2794376.

[153]

S.-C. LinS.-D. Lin and M.-S. Chen, A learning-based framework to handle multi-round multi-party influence maximization on social networks, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 695-704. doi: 10.1145/2783258.2783392.

[154]

Y. Lin and J. Lui, Algorithmic design for competitive influence maximization problems, arXiv preprint, arXiv: 1410.8664.

[155]

X. Ling, C. Wu, S. Ji and M. Han, H2dos: An application-layer dos attack towards http/2 protocol, in Proceedings of SecureComm: Security and Privacy in Communication Networks 2017, SecureComm '17, 2017.

[156]

B. LiuG. CongY. ZengD. Xu and Y. M. Chee, Influence spreading path and its application to the time constrained social influence maximization problem and beyond, Knowledge and Data Engineering, IEEE Transactions on, 26 (2014), 1904-1917. doi: 10.1109/TKDE.2013.106.

[157]

L. Liu, J. Tang, J. Han, M. Jiang and S. Yang, Mining topic-level influence in heterogeneous networks, in Proceedings of the 19th ACM International Conference on Information and Knowledge Management, ACM, Toronto, ON, Canada, 2010,199–208. doi: 10.1145/1871437.1871467.

[158]

L. LiuJ. TangJ. Han and S. Yang, Learning influence from heterogeneous social networks, Data Mining and Knowledge Discovery, 25 (2012), 511-544. doi: 10.1007/s10618-012-0252-3.

[159]

Q. LiuB. XiangE. ChenH. XiongF. Tang and J. X. Yu, Influence maximization over large-scale social networks: A bounded linear approach, CIKM '14 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, (2014), 171-180. doi: 10.1145/2661829.2662009.

[160]

X. Liu, M. Li, S. Li, S. Peng, X. Liao and X. Lu, Imgpu: Gpu accelerated influence maximization in large-scale social networks.

[161]

X. Liu, S. Li, X. Liao, L. Wang and Q. Wu, In-time estimation for influence maximization in large-scale social networks, in SNS '12 Proceedings of the Fifth Workshop on Social Network Systems, 2012, Article No. 3, 1–6. doi: 10.1145/2181176.2181179.

[162]

T. Lou and J. Tang, Mining structural hole spanners through information diffusion in social networks, 2013,825–836.

[163]

T. LouJ. TangJ. HopcroftZ. Fang and X. Ding, Learning to predict reciprocity and triadic closure in social networks, ACM Trans. Knowl. Discov. Data, 7 (2013), 1-25. doi: 10.1145/2499907.2499908.

[164]

J. -L. Lu, L. -Y. Wei and M. -Y. Yeh, Influence maximization in a social network in the presence of multiple influences and acceptances, 2014.

[165]

W. Lu, F. Bonchi, A. Goyal and L. V. S. Lakshmanan, The bang for the buck: Fair competitive viral marketing from the host perspective, in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Chicago, Illinois, USA, 2013,928–936. doi: 10.1145/2487575.2487649.

[166]

Y. Lu, J. Ren, J. Qian, M. Han, Y. Huo and T. Jing, Predictive contention window-based broadcast collision mitigation strategy for vanet, in Social Computing and Networking (SocialCom), 2016 IEEE International Conferences on, IEEE, 2016,209–215. doi: 10.1109/BDCloud-SocialCom-SustainCom.2016.41.

[167]

Y. Lu, Y. Zhu, M. Han, J. S. He and Y. Zhang, A survey of gpu accelerated svm, in Proceedings of the 2014 ACM Southeast Regional Conference, ACM, 2014, Article No. 15. doi: 10.1145/2638404.2638474.

[168]

Z. LuL. FanW. WuB. Thuraisingham and K. Yang, Efficient influence spread estimation for influence maximization under the linear threshold model, Computational Social Networks, 1 (2014), 1-19. doi: 10.1186/s40649-014-0002-3.

[169]

B. LucierJ. Oren and Y. Singer, Influence at scale: Distributed computation of complex contagion in networks, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2015), 735-744. doi: 10.1145/2783258.2783334.

[170]

Z. Z. M Han J Li, K-close: Algorithm for finding the close regions in wireless sensor networks based uncertain graph mining technology, Journal of Software, 22 (2011), 131-141.

[171]

K. Macropol and A. Singh, Scalable discovery of best clusters on large graphs, Proceedings of the VLDB Endowment, 3 (2010), 693-702. doi: 10.14778/1920841.1920930.

[172]

A. S. Maiya and T. Y. Berger-Wolf, Inferring the maximum likelihood hierarchy in social networks, in Proceedings of the 2009 International Conference on Computational Science and Engineering, vol. 4, IEEE Computer Society, 2009,245–250. doi: 10.1109/CSE.2009.235.

[173]

M. McPhersonL. Smith-Lovin and J. M. Cook, Birds of a feather: Homophily in social networks, Annual Review of Sociology, 27 (2001), 415-444. doi: 10.1146/annurev.soc.27.1.415.

[174]

I. Mele, F. Bonchi and A. Gionis, The early-adopter graph and its application to web-page recommendation, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, 2012, 1682–1686. doi: 10.1145/2396761.2398497.

[175]

S. MiharaS. Tsugawa and H. Ohsaki, Influence maximization problem for unknown social networks, ASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (2015), 1539-1546. doi: 10.1145/2808797.2808885.

[176]

S. Milgram, The small world problem, Psychology today, 2 (1967), 60-67.

[177]

A. Mislove, H. S. Koppula, K. P. Gummadi, P. Druschel and B. Bhattacharjee, Growth of the flickr social network, 2008, 25–30.

[178]

A. MisloveM. MarconK. P. GummadiP. Druschel and B. Bhattacharjee, Measurement and analysis of online social networks, IMC '07 Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, (2007), 29-42. doi: 10.1145/1298306.1298311.

[179]

E. Mossel and S. Roch, On the submodularity of influence in social networks, in Proceedings of the Thirty-Ninth Annual ACM Symposium on Theory of Computing, ACM, San Diego, California, USA, 2007,128–134. doi: 10.1145/1250790.1250811.

[180]

E. Mossel and G. Schoenebeck, Reaching consensus on social networks, 2010,214–229.

[181]

S. A. Myers and J. Leskovec, On the convexity of latent social network inference, threshold, 9 (2010), 20.

[182]

S. A. Myers and J. Leskovec, The bursty dynamics of the twitter information network, WWW '14 Proceedings of the 23rd International Conference on World Wide Web, (2014), 913-924. doi: 10.1145/2566486.2568043.

[183]

S. A. Myers, C. Zhu and J. Leskovec, Information diffusion and external influence in networks, in Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, 2012, 33–41. doi: 10.1145/2339530.2339540.

[184]

G. L. NemhauserL. A. Wolsey and M. L. Fisher, An analysis of approximations for maximizing submodular set functions. Ⅰ, Mathematical Programming, 14 (1978), 265-294. doi: 10.1007/BF01588971.

[185]

M. E. Newman, Spread of epidemic disease on networks, Physical Review E, 66 (2002), 016128, 11pp. doi: 10.1103/PhysRevE.66.016128.

[186]

M. E. Newman, The structure and function of complex networks, SIAM Review, 45 (2003), 167-256. doi: 10.1137/S003614450342480.

[187]

N. P. Nguyen, T. N. Dinh, X. Ying and M. T. Thai, Adaptive algorithms for detecting community structure in dynamic social networks, 2011 Proceedings IEEE INFOCOM, 2011. doi: 10.1109/INFCOM.2011.5935045.

[188]

N. P. Nguyen, G. Yan, M. T. Thai and S. Eidenbenz, Containment of misinformation spread in online social networks, in Proceedings of the 3rd Annual ACM Web Science Conference, ACM, Evanston, Illinois, 2012,213–222. doi: 10.1145/2380718.2380746.

[189]

J. Ok, Y. Jin, J. Choi, J. Shin and Y. Yi, Influence maximization over strategic diffusion in social networks, 2014 48th Annual Conference on Information Sciences and Systems (CISS), 2014. doi: 10.1109/CISS.2014.6814155.

[190]

J. P. Onnela and F. Reed-Tsochas, Spontaneous emergence of social influence in online systems, Proc Natl Acad Sci U S A, 107 (2010), 18375-18380. doi: 10.1073/pnas.0914572107.

[191]

L. Page, S. Brin, R. Motwani and T. Winograd, The pagerank citation ranking: Bringing order to the web.

[192]

W. PanW. DongM. CebrianT. KimJ. H. Fowler and A. S. Pentland, Modeling dynamical influence in human interaction: Using data to make better inferences about influence within social systems, Signal Processing Magazine, IEEE, 29 (2012), 77-86.

[193]

P. ParchasF. GulloD. Papadias and F. Bonchi, The pursuit of a good possible world: Extracting representative instances of uncertain graphs, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2014), 967-978. doi: 10.1145/2588555.2593668.

[194]

F. Paulsen, Tönnies, ferdinand. gemeinschaft und gesellschaft. abhandlung des communismus und des socialismus als empirischer culturformen. leipzig, fues's verlag, 1887, Vierteljahresschrift Für Wissenschaftliche Philosophie, 12 (1888), 111-119.

[195]

G. Ritzer and Others, The Blackwell Encyclopedia of Sociology vol. 1479, Blackwell Publishing Malden, MA, 2007.

[196]

M. G. Rodriguez, D. Balduzzi and B. Sch O Lkopf, Uncovering the temporal dynamics of diffusion networks, arXiv preprint, arXiv: 1105.0697.

[197]

M. G. Rodriguez, J. Leskovec and A. Krause, Inferring networks of diffusion and influence, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington, DC, USA, 2010, 1019–1028. doi: 10.1145/1835804.1835933.

[198]

M. G. Rodriguez and B. Sch O Lkopf, Influence maximization in continuous time diffusion networks, arXiv preprint, arXiv: 1205.1682.

[199]

D. M. Romero, W. Galuba, S. Asur and B. A. Huberman, Influence and passivity in social media, in Proceedings of the 20th International Conference Companion on World Wide Web, ACM, Hyderabad, India, 2011,113–114. doi: 10.1145/1963192.1963250.

[200]

Y. RongX. Wen and H. Cheng, A monte carlo algorithm for cold start recommendation, WWW '14 Proceedings of the 23rd International Conference on World Wide Web, (2014), 327-36. doi: 10.1145/2566486.2567978.

[201]

J. N. RosenquistJ. H. Fowler and N. A. Christakis, Social network determinants of depression, Molecular Psychiatry, 16 (2011), 273-281. doi: 10.1038/mp.2010.13.

[202]

R. A. Rossi, B. Gallagher, J. Neville and K. Henderson, Modeling dynamic behavior in large evolving graphs, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, ACM, Rome, Italy, 2013,667–676. doi: 10.1145/2433396.2433479.

[203]

M. Russell, Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, O'Reilly Media, 2011.

[204]

K. SaitoM. KimuraK. Ohara and H. Motoda, Efficient discovery of influential nodes for sis models in social networks, Knowledge and Information Systems, 30 (2012), 613-635. doi: 10.1007/s10115-011-0396-2.

[205]

K. Saito, M. Kimura, K. Ohara and H. Motoda, Learning asynchronous-time information diffusion models and its application to behavioral data analysis over social networks, Journal of Computer Engineering and Informatics, 1 (2013), 30–57, arXiv: 1204.4528. doi: 10.5963/JCEI0102002.

[206]

K. SaitoR. Nakano and M. Kimura, Prediction of information diffusion probabilities for independent cascade model, Knowledge-Based Intelligent Information and Engineering Systems, 5179 (2008), 67-75. doi: 10.1007/978-3-540-85567-5_9.

[207]

M. Salath EM. KazandjievaJ. W. LeeP. LevisM. W. Feldman and J. H. Jones, A high-resolution human contact network for infectious disease transmission, Proceedings of the National Academy of Sciences, 107 (2010), 22020-22025.

[208]

D. Sheldon, B. Dilkina, A. N. Elmachtoub, R. Finseth, A. Sabharwal, J. Conrad, C. P. Gomes, D. Shmoys, W. Allen, O. Amundsen and Others, Maximizing the spread of cascades using network design, arXiv preprint, arXiv: 1203.3514.

[209]

T. ShiS. ChengZ. CaiY. Li and J. Li, Retrieving the maximal time-bounded positive influence set from social networks, Personal and Ubiquitous Computing, 20 (2016), 717-730. doi: 10.1007/s00779-016-0943-7.

[210]

H. ShiokawaY. Fujiwara and M. Onizuka, Scan++: efficient algorithm for finding clusters, hubs and outliers on large-scale graphs, Proceedings of the VLDB Endowment, 8 (2015), 1178-1189. doi: 10.14778/2809974.2809980.

[211]

X. ShuaiY. DingJ. BusemeyerS. ChenY. Sun and J. Tang, Modeling indirect influence on twitter, International Journal on Semantic Web and Information Systems (IJSWIS), 8 (2012), 20-36. doi: 10.4018/jswis.2012100102.

[212]

Y. Singer, How to win friends and influence people, truthfully: Influence maximization mechanisms for social networks, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, ACM, Seattle, Washington, USA, 2012,733–742. doi: 10.4018/jswis.2012100102.

[213]

R. SiposA. Ghosh and T. Joachims, Was this review helpful to you?: It depends! context and voting patterns in online content, WWW '14 Proceedings of the 23rd International Conference on World Wide Web, (2014), 337-348. doi: 10.1145/2566486.2567998.

[214]

D. Song and D. A. Meyer, A model of consistent node types in signed directed social networks, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014. doi: 10.1109/ASONAM.2014.6921562.

[215]

G. SongX. ZhouY. Wang and K. Xie, Influence maximization on large-scale mobile social network: A divide-and-conquer method, Parallel and Distributed Systems, IEEE Transactions on, 26 (2015), 1379-1392. doi: 10.1109/TPDS.2014.2320515.

[216]

J. Stehl E, N. Voirin, A. Barrat, C. Cattuto, L. Isella, J. -F. C. C. O. Pinton, M. Quaggiotto, W. Van den Broeck, C. R E Gis, B. Lina and Others, High-resolution measurements of face-to-face contact patterns in a primary school, PloS one, 6 (2011), 23176.

[217]

J. Sun and J. Tang, A survey of models and algorithms for social influence analysis, in Social Network Data Analytics, Springer, 2011,177–214. doi: 10.1007/978-1-4419-8462-3_7.

[218]

T. Sun, W. Chen, Z. Liu, Y. Wang, X. Sun, M. Zhang and C. -Y. Lin, Participation maximization based on social influence in online discussion forums, 2011.

[219]

F. Tang, Q. Liu, H. Zhu, E. Chen and F. Zhu, Diversified social influence maximization, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014. doi: 10.1109/ASONAM.2014.6921625.

[220]

J. Tang, J. Sun, C. Wang and Z. Yang, Social influence analysis in large-scale networks, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Paris, France, 2009,807–816, 1557108. doi: 10.1145/1557019.1557108.

[221]

J. Tang, B. Wang, Y. Yang, P. Hu, Y. Zhao, X. Yan, B. Gao, M. Huang, P. Xu, W. Li and Others, Patentminer: topic-driven patent analysis and mining, KDD '12 Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012, 1366–1374. doi: 10.1145/2339530.2339741.

[222]

J. Tang, S. Wu and J. Sun, Confluence: Conformity influence in large social networks, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois, USA, 2013,347–355. doi: 10.1145/2487575.2487691.

[223]

J. Tang, C. Zhang, K. Cai, L. Zhang and Z. Su, Sampling representative users from large social networks, 2015.

[224]

J. TangY. ZhangJ. SunJ. RaoW. YuY. Chen and A. C. M. Fong, Quantitative study of individual emotional states in social networks, Affective Computing, IEEE Transactions on, 3 (2012), 132-144.

[225]

X. Tang and C. C. Yang, Ranking user influence in healthcare social media, ACM Trans. Intell. Syst. Technol., 3 (2012), Article No. 73. doi: 10.1145/2337542.2337558.

[226]

Y. TangX. Xiao and Y. Shi, Influence maximization: Near-optimal time complexity meets practical efficiency, SIGMOD '14 Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, (2014), 75-86. doi: 10.1145/2588555.2593670.

[227]

J. TeevanD. Ramage and M. R. Morris, Twittersearch: A comparison of microblog search and web search, WSDM '11 Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, (2011), 35-44. doi: 10.1145/1935826.1935842.

[228]

G. Tong, W. Wu, S. Tang and D. -Z. Du, Adaptive influence maximization in dynamic social networks, IEEE/ACM Transactions on Networking, 25 (2017), 112–125, arXiv: 1506.06294. doi: 10.1109/TNET.2016.2563397.

[229]

W. TongR. Goebel and G. Lin, Smoothed heights of tries and patricia tries, Theoretical Computer Science, 609 (2016), 620-626. doi: 10.1016/j.tcs.2015.02.009.

[230]

H. Trottier and P. Philippe, Deterministic modeling of infectious diseases: theory and methods, The Internet Journal of Infectious Diseases, 1 (2001), 3.

[231]

J. Tsai, T. H. Nguyen and M. Tambe, Security games for controlling contagion, 2012.

[232]

W. VerbekeD. Martens and B. Baesens, Social network analysis for customer churn prediction, Applied Soft Computing, 14 (2014), 431-446. doi: 10.1016/j.asoc.2013.09.017.

[233]

J. ViderasA. L. OwenE. Conover and S. Wu, The influence of social relationships on pro-environment behaviors, Journal of Environmental Economics and Management, 63 (2012), 35-50. doi: 10.1016/j.jeem.2011.07.006.

[234]

B. ViswanathA. MisloveM. Cha and K. P. Gummadi, On the evolution of user interaction in facebook, WOSN '09 Proceedings of the 2nd ACM Workshop on Online Social Networks, (2009), 37-472. doi: 10.1145/1592665.1592675.

[235]

R. W O Lfer and H. Scheithauer, Social influence and bullying behavior: Intervention-based network dynamics of the fairplayer. manual bullying prevention program, Aggressive behavior.

[236]

C. WangW. Chen and Y. Wang, Scalable influence maximization for independent cascade model in large-scale social networks, Data Mining and Knowledge Discovery, 25 (2012), 545-576. doi: 10.1007/s10618-012-0262-1.

[237]

F. Wang, E. Camacho and K. Xu, Positive influence dominating set in online social networks, in Proceedings of the 3rd International Conference on Combinatorial Optimization and Applications, Springer-Verlag, Huangshan, China, 5573 (2009), 313–321. doi: 10.1007/978-3-642-02026-1_29.

[238]

G. Wang, Q. Hu and P. S. Yu, Influence and similarity on heterogeneous networks, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, ACM, Maui, Hawaii, USA, 2012, 1462–1466. doi: 10.1145/2396761.2398453.

[239]

Y. Wang, G. Cong, G. Song and K. Xie, Community-based greedy algorithm for mining top-k influential nodes in mobile social networks, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Washington, DC, USA, 2010, 1039–1048. doi: 10.1145/1835804.1835935.

[240]

D. J. Watts and S. H. Strogatz, Collective dynamics of "small-world" networks, The Structure and Dynamics of Networks, (2011), 301-303. doi: 10.1515/9781400841356.301.

[241]

J. WengE. P. LimJ. Jiang and Q. He, Twitterrank: finding topic-sensitive influential twitterers, WSDM '10 Proceedings of the Third ACM International Conference on Web Search and Data Mining, (2010), 261-270. doi: 10.1145/1718487.1718520.

[242]

C. WilsonA. SalaK. P. N. Puttaswamy and B. Y. Zhao, Beyond social graphs: User interactions in online social networks and their implications, ACM Trans. Web, 6 (2012), 1-31. doi: 10.1145/2382616.2382620.

[243]

M. Workman, New media and the changing face of information technology use: The importance of task pursuit, social influence, and experience, Computers in Human Behavior, 31 (2014), 111-117. doi: 10.1016/j.chb.2013.10.008.

[244]

S. Wu, J. Sun and J. Tang, Patent partner recommendation in enterprise social networks, in Proceedings of the Sixth ACM International Conference on Web search and Data Mining, ACM, Rome, Italy, 2013, 43–52. doi: 10.1145/2433396.2433404.

[245]

X. XuN. YurukZ. Feng and T. A. J. Schweiger, Scan: a structural clustering algorithm for networks, KDD '07 Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2007), 824-833. doi: 10.1145/1281192.1281280.

[246]

M. Yan, M. Han, C. Ai, Z. Cai and Y. Li, Data aggregation scheduling in probabilistic wireless networks with cognitive radio capability, in IEEE GLOBECOM 2016, 2016. doi: 10.1109/GLOCOM.2016.7841716.

[247]

M. Yan, S. Ji, M. Han, Y. Li and Z. Cai, Data aggregation scheduling in wireless networks with cognitive radio capability, in Sensing, Communication, and Networking (SECON), 2014 Eleventh Annual IEEE International Conference on, IEEE, 2014,513–521.

[248]

D. -N. Yang, H. -J. Hung, W. -C. Lee and W. Chen, Maximizing acceptance probability for active friending in on-line social networks, arXiv preprint, arXiv: 1302.7025.

[249]

Y. Yang, J. Jia, S. Zhang, B. Wu, Q. Chen, J. Li, C. Xing and J. Tang, How do your friends on social media disclose your emotions?, 2014.

[250]

Y. Yang, J. Tang, C. Leung, Y. Sun, Q. Chen, J. Li and Q. Yang, Rain: Social role-aware information diffusion, 2015.

[251]

Z. YangJ. TangB. Xu and C. Xing, Active learning for networked data based on non-progressive diffusion model, WSDM '14 Proceedings of the 7th ACM International Conference on Web Search and Data Mining, (2014), 363-372. doi: 10.1145/2556195.2556223.

[252]

H. Yoganarasimhan, Impact of social network structure on content propagation: A study using youtube data, Quantitative Marketing and Economics, 10 (2012), 111-150. doi: 10.1007/s11129-011-9105-4.

[253]

H. Yu, S. -K. Kim and J. Kim, Scalable and parallelizable processing of influence maximization for large-scale social networks?, in Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013), IEEE Computer Society, 2013,266–277.

[254]

X. YuX. RenY. SunB. SturtU. KhandelwalQ. GuB. Norick and J. Han, Recommendation in heterogeneous information networks with implicit user feedback, RecSys '13 Proceedings of the 7th ACM Conference on Recommender Systems, (2013), 347-350. doi: 10.1145/2507157.2507230.

[255]

Y. Yu, T. Y. Berger-Wolf, J. Saia and Others, Finding spread blockers in dynamic networks, in Advances in Social Network Mining and Analysis, Springer, 2010, 55–76.

[256]

H. Zhang, A. D. Procaccia and Y. Vorobeychik, Dynamic influence maximization under increasing returns to scale, 2015.

[257]

H. Zhang, T. N. Dinh and M. T. Thai, Maximizing the spread of positive influence in online social networks, 2013 IEEE 33rd International Conference on Distributed Computing Systems, 2013. doi: 10.1109/ICDCS.2013.37.

[258]

H. Zhang, S. Mishra and M. T. Thai, Recent advances in information diffusion and influence maximization of complex social networks, Opportunistic Mobile Social Networks, 37.

[259]

J. Zhang, B. Liu, J. Tang, T. Chen and J. Li, Social influence locality for modeling retweeting behaviors, in Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, AAAI Press, Beijing, China, 2013, 2761–2767.

[260]

J. Zhang, J. Tang, C. Ma, H. Tong, Y. Jing and J. Li, Panther: Fast top-k similarity search in large networks, KDD '15 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, 1445–1454, arXiv: 1504.02577. doi: 10.1145/2783258.2783267.

[261]

J. Zhang, J. Tang, H. Zhuang, C. W. -K. Leung and J. Li, Role-aware conformity influence modeling and analysis in social networks, 2014.

[262]

M. ZhangJ. TangX. Zhang and X. Xue, Addressing cold start in recommender systems: A semi-supervised co-training algorithm, SIGIR '14 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, (2014), 73-82. doi: 10.1145/2600428.2609599.

[263]

P. ZhangW. ChenX. SunY. Wang and J. Zhang, Minimizing seed set selection with probabilistic coverage guarantee in a social network, KDD '14 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (2014), 1306-1315. doi: 10.1145/2623330.2623684.

[264]

J. ZhaoJ. WuX. FengH. Xiong and K. Xu, Information propagation in online social networks: A tie-strength perspective, Knowledge and Information Systems, 32 (2012), 589-608. doi: 10.1007/s10115-011-0445-x.

[265]

C. ZhouP. ZhangW. Zang and L. Guo, Maximizing the cumulative influence through a social network when repeat activation exists, Procedia Comput er Science, 29 (2014), 422-431. doi: 10.1016/j.procs.2014.05.038.

[266]

Y. Zhou and L. Liu, Social influence based clustering of heterogeneous information networks, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Chicago, Illinois, USA, 2013,338–346. doi: 10.1145/2487575.2487640.

[267]

H. Zhu, B. Huberman and Y. Luon, To switch or not to switch: Understanding social influence in online choices, in CHI '12, CHI '12, ACM, New York, NY, USA, 2012, 2257– 2266. doi: 10.1145/2207676.2208383.

[268]

H. ZhuangY. SunJ. TangJ. Zhang and X. Sun, Influence maximization in dynamic social networks, 2013 IEEE 13th International Conference on Data Mining, (2013), 1313-1318. doi: 10.1109/ICDM.2013.145.

Figure 1.  Publications Related Influence Analysis in the Recent Years
Figure 2.  A Social Network
Figure 3.  Common Neighbors in a Community
Figure 4.  Models of Social Influence by Different Networks
Figure 5.  Diffusion Models of Social Influence
Figure 6.  Probing Community for Dynamic Network
Figure 7.  Influence Maximization Models in Social Network
Figure 8.  Influence Diffusion Processing 1.
Figure 9.  Influence Diffusion Processing 2.
Figure 10.  Heterogeneous Models of Social Influence
Figure 11.  Models of Social Influence based on Biological Transmission
Figure 12.  Comprehensive Models of Social Influence
Figure 13.  Influence Analysis based Applications
Table 1.  Extensions or improvements of $IC/LT$ models
References Extender $IC$ $LT$ Remarks
Goyal [71] Learnt probability from the action log, simulation on both $IC$ and $LT$ models $\surd$ $\surd$
Chen et al.[42] Address the scalability issue, they proposed efficiency heuristic algorithm by restricting computations on the local influence regions of nodes $\surd$ $\times$ Showed that computing influence spread in the independent cascade model is #P-hard problem
Chen et al.[41] Extended the classical $IC$ model to study time-delayed influence diffusion $\surd$ $\times$ Their technical report version paper provides the NP-complete hardness of LT with their time-delay feature
Masahiro et al. [127] Improved the basic $IC$ and $LT$ by estimating marginal influence degrees $\surd$ $\surd$
Chen et al. [43] Degree discount heuristics achieve almost matching influence thread with the greedy algorithm, and run only in milliseconds which the traditional method run in hours $\surd$ $\surd$
Wang et al. [236] Heuristic algorithm for $IC$ model $\surd$ $\times$
Chen et al. [37] Extended the classical $IC$ model to incorporating negative opinions $\surd$ $\times$
Nam et al. [188] Focused on how to limit viral propagation of misinformation in OSNs $\surd$ $\surd$
Wang et al. [239] Extended $IC$ to mobile social networks, and use a dynamic programming algorithm to select communities then find influential nodes $\surd$ $\surd$
Kyomin et al. [121] Algorithm IRIE where IR for influence ranking, and IE for influence maximization are proposed to improve the classical algorithm developed previously $\surd$ $\times$ The algorithm was used in both classical $IC$ model and the extension $IC$-$N$ [37]
Thang [58] Extended the $LT$ model by constrain the influence distance as constant $d$ $\times$ $\surd$
References Extender $IC$ $LT$ Remarks
Goyal [71] Learnt probability from the action log, simulation on both $IC$ and $LT$ models $\surd$ $\surd$
Chen et al.[42] Address the scalability issue, they proposed efficiency heuristic algorithm by restricting computations on the local influence regions of nodes $\surd$ $\times$ Showed that computing influence spread in the independent cascade model is #P-hard problem
Chen et al.[41] Extended the classical $IC$ model to study time-delayed influence diffusion $\surd$ $\times$ Their technical report version paper provides the NP-complete hardness of LT with their time-delay feature
Masahiro et al. [127] Improved the basic $IC$ and $LT$ by estimating marginal influence degrees $\surd$ $\surd$
Chen et al. [43] Degree discount heuristics achieve almost matching influence thread with the greedy algorithm, and run only in milliseconds which the traditional method run in hours $\surd$ $\surd$
Wang et al. [236] Heuristic algorithm for $IC$ model $\surd$ $\times$
Chen et al. [37] Extended the classical $IC$ model to incorporating negative opinions $\surd$ $\times$
Nam et al. [188] Focused on how to limit viral propagation of misinformation in OSNs $\surd$ $\surd$
Wang et al. [239] Extended $IC$ to mobile social networks, and use a dynamic programming algorithm to select communities then find influential nodes $\surd$ $\surd$
Kyomin et al. [121] Algorithm IRIE where IR for influence ranking, and IE for influence maximization are proposed to improve the classical algorithm developed previously $\surd$ $\times$ The algorithm was used in both classical $IC$ model and the extension $IC$-$N$ [37]
Thang [58] Extended the $LT$ model by constrain the influence distance as constant $d$ $\times$ $\surd$
Table 1.  Extensions or improvements of $IC/LT$ models
References Extender $IC$ $LT$ Remarks
Chen et al. [44] A scalable heuristic algorithm for $LT$ were developed by constructing a local directed acyclic graphs (DAGs) $\times$ $\surd$ Showed that computing influence spread in the linear threshold model is #P-hard problem
Borodin et al. [22] Introduced $K$-$LT$ as the extension of $LT$ involved the competition of influence $\times$ $\surd$
He et al. [104] Under the $LT$ model, they extended it to influence blocking maximization problem $\times$ $\surd$
Goyal et al. [77] Improved the $LT$ by cutting down on the number of calls made in the first iteration which is the key to estimation procedure. $\times$ $\surd$
Goyal et al. [74] Under both $IC$ and $LT$ model, pursing the alternative goals which motivated by resource and time constraints $\surd$ $\surd$
Barbieri et al. [16] Extended both $IC$ and $LT$ to topic-aware models $\surd$ $\surd$
Wang et al. [238] Extended $IC$ to incorporate similarity in social network $\surd$ $\times$
Rodriguez et al. [198] General case of $IC$ model with time constraint $\surd$ $\times$
References Extender $IC$ $LT$ Remarks
Chen et al. [44] A scalable heuristic algorithm for $LT$ were developed by constructing a local directed acyclic graphs (DAGs) $\times$ $\surd$ Showed that computing influence spread in the linear threshold model is #P-hard problem
Borodin et al. [22] Introduced $K$-$LT$ as the extension of $LT$ involved the competition of influence $\times$ $\surd$
He et al. [104] Under the $LT$ model, they extended it to influence blocking maximization problem $\times$ $\surd$
Goyal et al. [77] Improved the $LT$ by cutting down on the number of calls made in the first iteration which is the key to estimation procedure. $\times$ $\surd$
Goyal et al. [74] Under both $IC$ and $LT$ model, pursing the alternative goals which motivated by resource and time constraints $\surd$ $\surd$
Barbieri et al. [16] Extended both $IC$ and $LT$ to topic-aware models $\surd$ $\surd$
Wang et al. [238] Extended $IC$ to incorporate similarity in social network $\surd$ $\times$
Rodriguez et al. [198] General case of $IC$ model with time constraint $\surd$ $\times$
[1]

Mirela Domijan, Markus Kirkilionis. Graph theory and qualitative analysis of reaction networks. Networks & Heterogeneous Media, 2008, 3 (2) : 295-322. doi: 10.3934/nhm.2008.3.295

[2]

Shuping Li, Zhen Jin. Impacts of cluster on network topology structure and epidemic spreading. Discrete & Continuous Dynamical Systems - B, 2017, 22 (10) : 3749-3770. doi: 10.3934/dcdsb.2017187

[3]

Liu Hui, Lin Zhi, Waqas Ahmad. Network(graph) data research in the coordinate system. Mathematical Foundations of Computing, 2018, 1 (1) : 1-10. doi: 10.3934/mfc.2018001

[4]

Deena Schmidt, Janet Best, Mark S. Blumberg. Random graph and stochastic process contributions to network dynamics. Conference Publications, 2011, 2011 (Special) : 1279-1288. doi: 10.3934/proc.2011.2011.1279

[5]

M. D. König, Stefano Battiston, M. Napoletano, F. Schweitzer. On algebraic graph theory and the dynamics of innovation networks. Networks & Heterogeneous Media, 2008, 3 (2) : 201-219. doi: 10.3934/nhm.2008.3.201

[6]

Mario Roy, Mariusz Urbański. Multifractal analysis for conformal graph directed Markov systems. Discrete & Continuous Dynamical Systems - A, 2009, 25 (2) : 627-650. doi: 10.3934/dcds.2009.25.627

[7]

Barton E. Lee. Consensus and voting on large graphs: An application of graph limit theory. Discrete & Continuous Dynamical Systems - A, 2018, 38 (4) : 1719-1744. doi: 10.3934/dcds.2018071

[8]

Luigi Fontana, Steven G. Krantz and Marco M. Peloso. Hodge theory in the Sobolev topology for the de Rham complex on a smoothly bounded domain in Euclidean space. Electronic Research Announcements, 1995, 1: 103-107.

[9]

A. C. Eberhard, J-P. Crouzeix. Existence of closed graph, maximal, cyclic pseudo-monotone relations and revealed preference theory. Journal of Industrial & Management Optimization, 2007, 3 (2) : 233-255. doi: 10.3934/jimo.2007.3.233

[10]

Mark G. Burch, Karly A. Jacobsen, Joseph H. Tien, Grzegorz A. Rempała. Network-based analysis of a small Ebola outbreak. Mathematical Biosciences & Engineering, 2017, 14 (1) : 67-77. doi: 10.3934/mbe.2017005

[11]

Chun Zong, Gen Qi Xu. Observability and controllability analysis of blood flow network. Mathematical Control & Related Fields, 2014, 4 (4) : 521-554. doi: 10.3934/mcrf.2014.4.521

[12]

Huan Su, Pengfei Wang, Xiaohua Ding. Stability analysis for discrete-time coupled systems with multi-diffusion by graph-theoretic approach and its application. Discrete & Continuous Dynamical Systems - B, 2016, 21 (1) : 253-269. doi: 10.3934/dcdsb.2016.21.253

[13]

Daniel Roggen, Martin Wirz, Gerhard Tröster, Dirk Helbing. Recognition of crowd behavior from mobile sensors with pattern analysis and graph clustering methods. Networks & Heterogeneous Media, 2011, 6 (3) : 521-544. doi: 10.3934/nhm.2011.6.521

[14]

Rui Hu, Yuan Yuan. Stability, bifurcation analysis in a neural network model with delay and diffusion. Conference Publications, 2009, 2009 (Special) : 367-376. doi: 10.3934/proc.2009.2009.367

[15]

Gheorghe Craciun, Baltazar Aguda, Avner Friedman. Mathematical Analysis Of A Modular Network Coordinating The Cell Cycle And Apoptosis. Mathematical Biosciences & Engineering, 2005, 2 (3) : 473-485. doi: 10.3934/mbe.2005.2.473

[16]

Rumi Ghosh, Kristina Lerman. Rethinking centrality: The role of dynamical processes in social network analysis. Discrete & Continuous Dynamical Systems - B, 2014, 19 (5) : 1355-1372. doi: 10.3934/dcdsb.2014.19.1355

[17]

Yacine Chitour, Frédéric Grognard, Georges Bastin. Equilibria and stability analysis of a branched metabolic network with feedback inhibition. Networks & Heterogeneous Media, 2006, 1 (1) : 219-239. doi: 10.3934/nhm.2006.1.219

[18]

Thomas Hillen, Peter Hinow, Zhi-An Wang. Mathematical analysis of a kinetic model for cell movement in network tissues. Discrete & Continuous Dynamical Systems - B, 2010, 14 (3) : 1055-1080. doi: 10.3934/dcdsb.2010.14.1055

[19]

D. Warren, K Najarian. Learning theory applied to Sigmoid network classification of protein biological function using primary protein structure. Conference Publications, 2003, 2003 (Special) : 898-904. doi: 10.3934/proc.2003.2003.898

[20]

Jerrold E. Marsden, Alexey Tret'yakov. Factor analysis of nonlinear mappings: p-regularity theory. Communications on Pure & Applied Analysis, 2003, 2 (4) : 425-445. doi: 10.3934/cpaa.2003.2.425

 Impact Factor: 

Article outline

Figures and Tables

[Back to Top]