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A Holistic Approach to Influence Maximization

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Abstract

A social network is an Internet-based collaboration platform that plays a vital role in information spread, opinion-forming, trend-setting, and keeps everyone connected. Moreover, the popularity of web and social networks has interesting applications including viral marketing, recommendation systems, poll analysis, etc. In these applications, user influence plays an important role. This chapter discusses how effectively social networks can be used for information propagation in the context of viral marketing. Picking the right group of users, hoping they will cause a chain effect of marketing, is the core of viral marketing applications. The strategy used to select the correct group of users is the influence maximization problem.

This chapter proposes one of the viable solutions to influence maximization. The focus is to find those users in the social networks who would adopt and propagate information, thus resulting in an effective marketing strategy. The three main components that would help in the effective spread of information in the social networks are: the network structure, the user’s influence on others, and the seeding algorithm. Amalgamation of these three aspects provides a holistic solution to influence maximization.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/research/people/weic/#publications

  2. 2.

    http://socialcomputing.asu.edu

  3. 3.

    http://tuvalu.santafe.edu/~aaronc/powerlaws

References

  1. Andrews, J.D., Beeson, S.: Birnbaum’s measure of component importance for noncoherent systems. IEEE Trans. Reliab. 52(2), 213–219 (2003)

    Article  Google Scholar 

  2. Arenas, A., Duch, J., Fernandez, A., Gomez, S.: Size reduction of complex networks preserving modularity. CoRR (2007). abs/physics/0702015

    Google Scholar 

  3. Borgatti, S.P., Carley, K.M., Krackhardt, D.: On the robustness of centrality measures under conditions of imperfect data. Soc. Netw. 28(2), 124–136 (2006)

    Article  Google Scholar 

  4. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD, KDD ’09, pp. 199–208. ACM, New York, NY (2009). doi:10.1145/1557019.1557047

    Google Scholar 

  5. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD, KDD ’10, pp. 1029–1038. ACM, New York, NY (2010) doi:10.1145/1835804.1835934

    Google Scholar 

  6. Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10, pp. 88–97 (2010)

    Google Scholar 

  7. Chen, W., Lin, T., Tan, Z., Zhao, M., Zhou, X.: Robust influence maximization. CoRR (2016). abs/1601.06551. http://arxiv.org/abs/1601.06551

  8. Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51(4), 661–703 (2009). doi:10.1137/070710111. http://dx.doi.org/10.1137/070710111

    Article  MathSciNet  MATH  Google Scholar 

  9. Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the Seventh ACM SIGKDD, KDD ’01, pp. 57–66. ACM, New York, NY (2001). doi:10.1145/502512.502525

    Google Scholar 

  10. Fang, X., Hu, P.J.H., Li, Z., Tsai, W.: Predicting adoption probabilities in social networks. Inf. Syst. Res. 24(1), 128–145 (2013)

    Article  Google Scholar 

  11. Foti, N.J., Hughes, J.M., Rockmore, D.N.: Nonparametric sparsification of complex multiscale networks. PLoS One 6(2), 16431 (2011). doi:10.1371/journal.pone.0016431

    Article  Google Scholar 

  12. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)

    Article  Google Scholar 

  13. Fung, W.S., Hariharan, R., Harvey, N.J.A., Panigrahi, D.: A general framework for graph sparsification. In: Fortnow, L., Vadhan, S.P. (eds.) STOC. pp. 71–80. ACM, New York, NY (2011)

    Google Scholar 

  14. Ganesan, K.: Case study on ripple effects of ice bucket challenge on social media channels (2016). http://www.digitalvidya.com/blog/

    Google Scholar 

  15. Gargano, L., Hell, P., Peters, J., Vaccaro, U.: Influence diffusion in social networks under time window constraints. In: Structural Information and Communication Complexity: 20th International Colloquium, SIROCCO 2013, Ischia, July 1–3, 2013. Revised Selected Papers

    Google Scholar 

  16. Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM ’10, pp. 241–250. ACM, New York, NY (2010)

    Google Scholar 

  17. Goyal, A., Lu, W., Lakshmanan, L.V.: CELF++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW ’11, pp. 47–48. ACM, New York, NY (2011). doi:10.1145/1963192.1963217

    Google Scholar 

  18. He, X., Kempe, D.: Robust influence maximization. CoRR (2016). abs/1602.05240 http://arxiv.org/abs/1602.05240

  19. Heidemann, J., Klier, M., Probst, F.: Identifying key users in online social networks: a pagerank based approach. In: Sabherwal, R., Sumner, M. (eds.) ICIS, p. 79. Association for Information Systems (2010)

    Google Scholar 

  20. Jiang, J., Wilson, C., Wang, X., Sha, W., Huang, P., Dai, Y., Zhao, B.Y.: Understanding latent interactions in online social networks. ACM Trans. Web 7(4), 18 (2013)

    Article  Google Scholar 

  21. Johnson, T.: Mathematical modeling of diseases: susceptible-infected-recovered (sir) model (2009). http://op12no2.me/stuff/tjsir.pdf

    Google Scholar 

  22. Jung, K., Heo, W., Chen, W.: IRIE: a scalable influence maximization algorithm for independent cascade model and its extensions. CoRR (2011). abs/1111.4795

    Google Scholar 

  23. Kasthurirathna, D., Harre, M., Piraveenan, M.: Influence modelling using bounded rationality in social networks. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 33–40. ACM, New York, NY (2015)

    Google Scholar 

  24. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD, KDD ’03, pp. 137–146. ACM, New York, NY (2003). doi:10.1145/956750.956769

    Google Scholar 

  25. Kempe, D., Kleinberg, J., Tardos, E.: Influential nodes in a diffusion model for social networks. In: Proceedings of the 32Nd International Conference on Automata, Languages and Programming, ICALP’05, pp. 1127–1138. Springer, Berlin, Heidelberg (2005)

    Google Scholar 

  26. Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics. ii. The problem of endemicity. Proc. R. Soc. Lond. 138(834), (1932). doi:10.1098/rspa.1932.0171

    Google Scholar 

  27. Kimura, M., Saito, K., Motoda, H.: Efficient estimation of influence functions for sis model on social networks. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, IJCAI’09, pp. 2046–2051. Morgan Kaufmann, San Francisco, CA (2009)

    Google Scholar 

  28. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  29. Kutzkov, K., Bifet, A., Bonchi, F., Gionis, A.: Strip: stream learning of influence probabilities. In: Proceedings of the 19th ACM SIGKDD, KDD ’13, pp. 275–283. ACM, New York, NY (2013)

    Google Scholar 

  30. Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD, KDD ’07, pp. 420–429. ACM, New York, NY (2007)

    Google Scholar 

  31. Lisa, R.: Social influence. In: The Blackwell Encyclopedia of Sociology, pp. 4426–4429. Oxford Blackwell, Malden, MA (2008)

    Google Scholar 

  32. Liu, B., Cong, G., Xu, D., Zeng, Y.: Time constrained influence maximization in social networks. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), IEEE, pp. 439–448 (2012)

    Google Scholar 

  33. Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of influence networks. In: Proceedings of the 17th ACM SIGKDD, KDD ’11, pp. 529–537. ACM, New York, NY (2011)

    Google Scholar 

  34. McCracken, G.: How ford got social marketing right (2010). https://hbr.org/2010/01/ford-recently-wrapped-the-firs/

    Google Scholar 

  35. Misiolek, E., Chen, D.Z.: Two flow network simplification algorithms. Inf. Process. Lett. 97(5), 197–202 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  36. Mullaney, T.: Social media is reinventing how business is done (2012). http://www.usatoday.com/money/economy/story/2012-05-14/social-media-economy-companies/55029088/1/

    Google Scholar 

  37. Myerson, R.: Graphs and cooperation in games. In: Dutta, B., Jackson, M. (eds.) Networks and Groups, Studies in Economic Design, pp. 17–22. Springer, Berlin, Heidelberg (2003). doi:10.1007/978-3-540-24790-6_2

    Chapter  Google Scholar 

  38. Nguyen, H., Zheng, R.: Influence spread in large-scale social networks–a belief propagation approach. In: Machine Learning and Knowledge Discovery in Databases, pp. 515–530. Springer, Berlin (2012)

    Google Scholar 

  39. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Standford Infolab (1999)

    Google Scholar 

  40. Pham-Gia, T., Hung, T.: The mean and median absolute deviations. Math. Comput. Model. 34(7–8), 921–936 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  41. Qin, Y., Ma, J., Gao, S.: Efficient influence maximization under TSCM: a suitable diffusion model in online social networks. Soft Comput. 1–12 (2016). doi:10.1007/s00500-016-2068-3

    Google Scholar 

  42. Quirin, A., Cordn, O., Santamara, J., Vargas-Quesada, B., Moya-Anegn, F.: A new variant of the pathfinder algorithm to generate large visual science maps in cubic time. Inf. Process. Manage. 44(4), 1611–1623 (2008)

    Article  Google Scholar 

  43. 1, 215–239 (2008) Robert, H.: Applicability of graph metrics when analyzing online social networks. Curr. Issues IT-Manage. 1, 215–239 (2008)

    Google Scholar 

  44. Romero, D.M., Galuba, W., Asur, S., Huberman, B.A.: Influence and passivity in social media. In: Machine learning and knowledge discovery in databases, pp. 18–33. Springer, Berlin (2011)

    Google Scholar 

  45. Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R., Jain, L. (eds.) Knowledge-Based Intelligent Information and Engineering Systems. Lecture Notes in Computer Science, vol. 5179, pp. 67–75. Springer, Berlin, Heidelberg (2008). doi:10.1007/978-3-540-85567-5_9

    Chapter  Google Scholar 

  46. Saito, K., Kimura, M., Ohara, K., Motoda, H.: Efficient estimation of cumulative influence for multiple activation information diffusion model with continuous time delay. In: PRICAI 2010: Trends in Artificial Intelligence, Daegu, pp. 244–255 (2010)

    Google Scholar 

  47. Serrano, M.A., Bog, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. Proc. Natl. Acad. Sci. 106(16), 6483–6488 (2009)

    Article  Google Scholar 

  48. Smith, C.: How many people use the top social media, apps & services (2014). Http://expandedramblings.com

    Google Scholar 

  49. Subbian, K., Aggarwal, C., Srivastava, J.: Mining influencers using information flows in social streams. ACM Trans. Knowl. Discov. Data 10(3), 26:1–26:28 (2016). doi:10.1145/2815625

    Google Scholar 

  50. Sumith, N., Annappa, B., Bhattacharya, S.: Social network pruning for building optimal social network: a user perspective. Knowl. Based Syst. 117, 101–110 (2017)

    Article  Google Scholar 

  51. Teng, Y.W., Tai, C.H., Yu, P.S., Chen, M.S.: Modeling and utilizing dynamic influence strength for personalized promotion. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 57–64. ACM, New York, NY (2015)

    Google Scholar 

  52. Treagus, P.: The dark knight: a case study of viral marketing (2014). http://philtreagus.com/the-dark-knight-a-case-study-of-viral-marketing/

    Google Scholar 

  53. Wang, Z., Qian, Z., Lu, S.: A probability based algorithm for influence maximization in social networks. In: Proceedings of the 5th Asia-Pacific Symposium on Internetware, Internetware ’13, pp. 12:1–12:7. ACM, New York, NY (2013)

    Google Scholar 

  54. Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: User interactions in social networks and their implications. In: Proceedings of the 4th ACM European Conference on Computer systems, pp. 205–218. ACM, New York, NY (2009)

    Google Scholar 

  55. Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 981–990. ACM, New York, NY (2010)

    Google Scholar 

  56. Yang, J., Leskovec, J.: Modeling information diffusion in implicit networks. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 599–608. IEEE, New York, NY (2010)

    Google Scholar 

  57. Zhang, H., Mishra, S., Thai, M.T., Wu, J., Wang, Y.: Recent advances in information diffusion and influence maximization in complex social networks. Oppor. Mobile Soc. Netw. 37 (1.1) (2014)

    Google Scholar 

  58. Zhou, F., Malher, S., Toivonen, H.: Network simplification with minimal loss of connectivity. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 659–668 (2010). doi:10.1109/ICDM.2010.133

    Google Scholar 

  59. Zhou, F., Mahler, S., Toivonen, H.: Review of bisonet abstraction techniques. In: Bisociative Knowledge Discovery, pp. 166–178. Springer, Berlin (2012)

    Google Scholar 

  60. Zhou, F., Mahler, S., Toivonen, H.: Simplification of networks by edge pruning. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. Lecture Notes in Computer Science, vol. 7250, pp. 179–198. Springer, Berlin (2012)

    Chapter  Google Scholar 

  61. Zhuang, H., Sun, Y., Tang, J., Zhang, J., Sun, X.: Influence maximization in dynamic social networks. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1313–1318. IEEE, New York (2013)

    Google Scholar 

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, Mangaluru, India

    Nireshwalya Sumith & Basava Annappa

  2. Jadavpur University, Kolkata, India

    Swapan Bhattacharya

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  1. Nireshwalya Sumith
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  2. Basava Annappa
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  3. Swapan Bhattacharya
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Correspondence to Nireshwalya Sumith .

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Editors and Affiliations

  1. Dept. of Computer Science, Dyal Singh College, University of Delhi, Delhi, India

    Hema Banati

  2. Dept. of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  3. Dept. of Electrical & Electronics Engineering, Amity University, Noida, Uttar Pradesh, India

    Ashish Mani

  4. Network Design and Research Center, Kyushu Institute of Technology, Fukuoka, Japan

    Mario Köppen

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Sumith, N., Annappa, B., Bhattacharya, S. (2017). A Holistic Approach to Influence Maximization. In: Banati, H., Bhattacharyya, S., Mani, A., Köppen, M. (eds) Hybrid Intelligence for Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-65139-2_6

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