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Introduction of Social Influence Analysis

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Optimal Social Influence

Part of the book series: SpringerBriefs in Optimization ((BRIEFSOPTI))

Abstract

With the emergence and rapid proliferation of social applications and media, such as instant messaging (e.g., WhatsApp, Viber, WeChat, Snapchat, Line, Facebook Messenger, and Google Hangouts), sharing sites (e.g., Flickr, YouTube, and Yelp), blogs (e.g., WordPress and LiveJournal), wikis (e.g., Wikipedia and PBWiki), microblogs (e.g., Twitter and Weibo), social networks (e.g., Facebook), and collaboration networks (e.g., DBLP), there is little doubt that social influence is becoming a prevalent, complex, and subtle force that governs the dynamics of all social networks. Therefore, social influence study has started to attract intense attention due to many important applications.

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References

  1. L.A. Adamic, O. Buyukkokten, E. Adar, A social network caught in the web. First Monday 8, 6 (2003)

    Article  Google Scholar 

  2. R. Albert, H. Jeong, A.L. Barabasi, The diameter of the world wide web. Nature 401, 130 (1999)

    Article  Google Scholar 

  3. L.A.N. Amaral, A. Scala, M. Barthelemy, H.E. Stanley, Classes of small-world networks. Proc. Natl. Acad. Sci. (PNAS) 97, 11149–11152 (2000)

    Article  Google Scholar 

  4. A. Anagnostopoulos, R. Kumar, M. Mahdian, Influence and correlation in social networks, in Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), vol. 0 (2008), pp. 7–15

    Google Scholar 

  5. S. Aral, D. Walker, Identifying influential and susceptible members of social networks. Science 337, 337–341 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  6. S. Aral, L. Muchnika, A. Sundararajan, Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc. Natl. Acad. Sci. 106(51), 21544–21549 (2009)

    Article  Google Scholar 

  7. L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan, Group formation in large social networks: membership, growth, and evolution, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Philadelphia, PA (2006)

    Book  Google Scholar 

  8. L. Backstrom, R. Kumar, C. Marlow, J. Novak, A. Tomkins, Preferential behavior in online groups, in Proceedings of the International Conference on Web Search and Web Data Mining (WSDM) (2008), pp. 117–128

    Google Scholar 

  9. A.L. Barabasi, R. Albert, Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  10. R.M. Bond, C.J. Fariss, J.J. Jones, A.D.I. Kramer, C. Marlow, J.E. Settle, J.H. Fowler, A 61-million-person experiment in social influence and political mobilization. Nature 489, 295–298 (2012)

    Article  Google Scholar 

  11. S.P. Borgatti, M.G. Everett, A graph-theoretic perspective on centrality. Soc. Netw. 28(4), 466–484 (2006)

    Article  Google Scholar 

  12. V. Braitenberg, A. Schuz, Anatomy of a Cortex: Statistics and Geometry (Springer, Berlin, 1991)

    Book  Google Scholar 

  13. U. Brandes, A faster algorithm for betweenness centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    Article  MATH  Google Scholar 

  14. A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, J. Wiener, Graph structure in the web: Experiments and models, in Proceedings of the 9th International World Wide Web Conference (WWW) (2000)

    Book  Google Scholar 

  15. R.S. Burt, Structural Holes: The Social Structure of Competition (Harvard University Press, Cambridge, 1992)

    Google Scholar 

  16. J.T. Cacioppo, J.H. Fowler, N.A. Christakis, Alone in the crowd: the structure and spread of loneliness in a large social network. SSRN eLibrary (2008)

    Google Scholar 

  17. M. Cha, H. Haddadi, F. Benevenuto, K. Gummadi, Measuring user influence in twitter: the million follower fallacy, in Proceedings of the 4th International Conference on Weblogs and Social Media (2010)

    Google Scholar 

  18. H. Chen, R.H.L. Chiang, V.C. Storey, Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)

    Article  Google Scholar 

  19. N.A. Christakis, J.H. Fowler, The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357(4), 370–379 (2007)

    Article  Google Scholar 

  20. N.A. Christakis, J.H. Fowler, The collective dynamics of smoking in a large social network. N. Engl. J. Med. 358(21), 2249–2258 (2008)

    Article  Google Scholar 

  21. R.B. Cialdini, N.J. Goldstein, Social influence: compliance and conformity. Annu. Rev. Psychol. 55, 591–621 (2004)

    Article  Google Scholar 

  22. D. Crandall, D. Cosley, D. Huttenlocher, J. Kleinberg, S. Suri, Feedback effects between similarity and social influence in online communities, in Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2008), pp. 160–168

    Google Scholar 

  23. P. Domingos, M. Richardson, Mining the network value of customers, in Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2001), pp. 57–66

    Google Scholar 

  24. P.W. Eastwick, W.L. Gardner, Is it a game? Evidence for social influence in the virtual world. Soc. Influ. 4(1), 18–32 (2009)

    Google Scholar 

  25. S.M. Elias, A.R. Pratkanis, Teaching social influence: demonstrations and exercises from the discipline of social psychology. Soc. Influ. 1(2), 147–162 (2006)

    Article  Google Scholar 

  26. M. Faloutsos, P. Faloutsos, C. Faloutsos, On power-law relationships of the internet topology, in Proceedings of the Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM) (1999), pp. 251–262

    Article  MATH  Google Scholar 

  27. T.L. Fond, J. Neville, Randomization tests for distinguishing social influence and homophily effects, in Proceeding of the 19th International Conference on World Wide Web (WWW) (2010), pp. 601–610

    Google Scholar 

  28. J.H. Fowler, N.A. Christakis, The dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. Br. Med. J. 337, a2338 (2008)

    Article  Google Scholar 

  29. L.C. Freeman, A set of measure of centrality based on betweenness. Sociometry 40, 35 (1977)

    Article  Google Scholar 

  30. L.C. Freeman, Centrality in social networks: conceptual clarification. Soc. Netw. 1, 215–239 (1979)

    Article  Google Scholar 

  31. M. Girvan, M.E.J. Newman, Community structure in social and biological networks. Proc. Natl. Acad. Sci. (PNAS) 99, 7821–7826 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  32. A. Goyal, F. Bonchi, L.V. Lakshmanan, Learning influence probabilities in social networks, in Proceedings of the 3rd ACM International Conference on Web Search and Data Mining (2010), pp. 241–250

    Google Scholar 

  33. M. Granovetter, The strength of weak ties. Am. J. Sociol. 78(6), 1360 (1973)

    Article  Google Scholar 

  34. M. Granovetter, Economic action and social structure: the problem of embeddedness. Am. J. Sociol. 91(3), 481–510 (1985)

    Article  Google Scholar 

  35. B. Hajian, T. White, Modelling influence in a social network: Metrics and evaluation, in IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing (2011), pp. 497–500

    Google Scholar 

  36. P. Holme, M.E.J. Newman, Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. 74, 056–108 (2006)

    Google Scholar 

  37. A. Java, P. Kolari, T. Finin, T. Oates, Modeling the spread of influence on the blogosphere, in Proceeding of the 15th International Conference on World Wide Web (WWW) (2006)

    Google Scholar 

  38. L. Katz, A new index derived from sociometric data analysis. Psychometrika 18, 39–43 (1953)

    Article  MATH  Google Scholar 

  39. D. Kempe, J. Kleinberg, E. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2003), pp. 137–146

    Google Scholar 

  40. A. Khrabrov, G. Cybenko, Discovering influence in communication networks using dynamic graph analysis, in Social Computing/IEEE International Conference on Privacy, Security, Risk and Trust, vol. 0 (2010), pp. 288–294

    Google Scholar 

  41. J. Kleinberg, Navigation in a small world. Nature 406, 845–855 (2000)

    Article  Google Scholar 

  42. J. Kleinberg, The small-world phenomenon: An algorithmic perspective, in Proceedings of the 32nd ACM Symposium on Theory of Computing (STOC) (2000)

    Book  Google Scholar 

  43. J. Kleinberg, S. Lawrence, Authoritative sources in a hyperlinked environment. J. ACM 46, 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  44. J. Kleinberg, S. Lawrence, The structure of the web. Science 294, 1849–1850 (2001)

    Article  Google Scholar 

  45. R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins, Trawling the web for emerging cyber-communities. Comput. Netw. 31, 1481–1493 (1999)

    Article  Google Scholar 

  46. R. Kumar, J. Novak, A. Tomkins, Structure and evolution of online social networks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Philadelphia, PA (2006)

    Google Scholar 

  47. P. Lazarsfeld, R.K. Merton, Friendship as a social process: a substantive and methodological analysis. Freedom Control Mod. Soc. 18, 18–66 (1954)

    Google Scholar 

  48. L. Li, D. Alderson, J.C. Doyle, W. Willinger, Towards a theory of scale-free graphs: definitions, properties, and implications. Internet Math. 2(4), 431–523 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  49. D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, A. Tomkins, Geographic routing in social networks. Proc. Natl. Acad. Sci. (PNAS) 102(33), 11623–11628 (2005)

    Article  Google Scholar 

  50. T. Lou, J. Tang, J. Hopcroft, Z. Fang, X. Ding, Learning to predict reciprocity and triadic closure in social networks. ACM Trans. Knowl. Discov. Data (TKDD) 7(2), 5 (2013)

    Article  Google Scholar 

  51. S.C. Mednick, N.A. Christakis, J.H. Fowler, The spread of sleep loss influences drug use in adolescent social networks. PLoS One 5(3), e9775 (2010)

    Article  Google Scholar 

  52. S. Milgram, The small world problem. Psychol. Today 2, 60 (1967)

    Google Scholar 

  53. J. Neville, O. Simsek, D. Jensen, Autocorrelation and relational learning: challenges and opportunities, in Proceedings of the ICML-04 Workshop on Statistical Relational Learning (2004)

    Book  Google Scholar 

  54. M.E.J. Newman, The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. (PNAS) 98, 409–415 (2001)

    MathSciNet  MATH  Google Scholar 

  55. M.E.J. Newman, A measure of betweenness centrality based on random walks. Soc. Netw. 27(1), 39–54 (2005)

    Article  Google Scholar 

  56. L. Page, S. Brin, R. Motwani, T. Winograd, The pagerank citation ranking: bringing order to the web. Technical report, Stanford University (1998)

    Google Scholar 

  57. A.G. Phadke, J.S. Thorp, Computer Relaying for Power Systems (Wiley, Chichester, 1988)

    Google Scholar 

  58. I. Pool, M. Kochen, Contacts and influence. Soc. Netw. 1, 1–48 (1978)

    Article  MathSciNet  Google Scholar 

  59. A. Rad, M. Benyoucef, Towards detecting influential users in social networks, in E-Technologies: Transformation in a Connected World: 5th International Conference, MCETECH, Les Diablerets, vol. 78 (2011)

    Google Scholar 

  60. M. Richardson, P. Domingos, Mining knowledge-sharing sites for viral marketing, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (ACM, New York, 2002)

    Google Scholar 

  61. J.N. Rosenquist, J. Murabito, J.H. Fowler, N.A. Christakis, The spread of alcohol consumption behavior in a large social network. Ann. Internal Med. 152(7), 426–W141 (2010)

    Article  Google Scholar 

  62. P. Sarkar, A. Moore, Dynamic social network analysis using latent space models, in ACM SIGKDD Explorations Newsletter, vol. 7(2) (2005), pp. 31–40

    Article  Google Scholar 

  63. J. Scripps, P.N. Tan, A.H. Esfahanian, Measuring the effects of preprocessing decisions and network forces in dynamic network analysis, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2009), pp. 747–756

    Google Scholar 

  64. X. Shi, J. Zhu, R. Cai, L. Zhang, User grouping behavior in online forums, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2009), pp. 777–785

    Google Scholar 

  65. G. Siganos, S.L. Tauro, M. Faloutsos, Jellyfish: a conceptual model for the as internet topology. J. Commun. Netw. 8(3), 339–350 (2006)

    Article  Google Scholar 

  66. P. Singla, M. Richardson, Yes, there is a correlation: from social networks to personal behavior on the web, in Proceeding of the 17th International Conference on World Wide Web (WWW) (2008), pp. 655–664

    Google Scholar 

  67. J. Sun, J. Tang, A survey of models and algorithms for social influence analysis, in Social Network Data Analytics (Springer, Boston, 2011), pp. 177–214

    Google Scholar 

  68. L. Tang, H. Liu, Relational learning via latent social dimensions, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2009), pp. 817–826

    Google Scholar 

  69. L. Tang, H. Liu, Scalable learning of collective behavior based on sparse social dimensions, in Proceeding of the 18th ACM Conference on Information and Knowledge Management (CIKM) (2009), pp. 1107–1116

    Google Scholar 

  70. J. Tang, J. Sun, C. Wang, Z. Yang, Social influence analysis in large-scale networks, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009), pp. 807–816

    Google Scholar 

  71. C.E. Tsourakakis, Fast counting of triangles in large real networks without counting: Algorithms and laws, in Eighth IEEE International Conference on Data Mining, (2008), pp. 608–617

    Google Scholar 

  72. J. Ugandera, L. Backstromb, C. Marlowb, J. Kleinberg, Structural diversity in social contagion. Proc. Natl. Acad. Sci. 109(20), 7591–7592 (2012)

    Article  Google Scholar 

  73. S. Wasserman, K. Faust, Social Networks Analysis: Methods and Applications (Cambridge University Press, Cambridge, 1994)

    Book  MATH  Google Scholar 

  74. D.J. Watts, S.H. Strogatz, Collective dynamics of small-world networks. Nature 393, 440–442 (1998)

    Article  MATH  Google Scholar 

  75. C. Wildeman, A.V. Papachristos, Network exposure and homicide victimization in an African American community. Am. J. Public Health 337, 337 (2013)

    Google Scholar 

  76. X. Wu, X. Zhu, G.Q. Wu, W. Ding, Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  77. R. Xiang, J. Neville, M. Rogati, Modeling relationship strength in online social networks, in Proceeding of the 19th International Conference on World Wide Web (WWW) (2010), pp. 981–990

    Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Texas Woman’s University, Denton, TX, USA

    Wen Xu

  2. Department of Computer Science, University of Texas, Dallas, Richardson, TX, USA

    Weili Wu

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  1. Wen Xu
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Xu, W., Wu, W. (2020). Introduction of Social Influence Analysis. In: Optimal Social Influence. SpringerBriefs in Optimization. Springer, Cham. https://doi.org/10.1007/978-3-030-37775-5_1

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