Advertisement

Social Network User Influence Dynamics Prediction

  • Jingxuan Li
  • Wei Peng
  • Tao Li
  • Tong Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)

Abstract

Identifying influential users and predicting their “network impact” on social networks have attracted tremendous interest from both academia and industry. Most of the developed algorithms and tools are mainly dependent on the static network structure instead of the dynamic diffusion process over the network, and are thus essentially based on descriptive models instead of predictive models. In this paper, we propose a dynamic information propagation model based on Continuous-Time Markov Process to predict the influence dynamics of social network users, where the nodes in the propagation sequences are the users, and the edges connect users who refer to the same topic contiguously on time. Our proposed model is compared with two baselines, including a well-known time-series forecasting model – Autoregressive Integrated Moving Average and a widely accepted information diffusion model – Independent cascade. Experimental results validate our ideas and demonstrate the prediction performance of our proposed algorithms.

Keywords

Social media Influential users Dynamic information diffusion Continuous-Time Markov Process 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adamic, L.A., Adar, E.: How to search a social network. Social Networks 27, 2005 (2005)Google Scholar
  2. 2.
    Anderson, W.J., James, W.: Continuous-time Markov chains: An applications-oriented approach, vol. 7. Springer, New York (1991)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM, pp. 65–74 (2011)Google Scholar
  4. 4.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in twitter: The million follower fallacy. In: AAAI, ICWSM (2010)Google Scholar
  5. 5.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: SIGKDD, pp. 57–66. ACM (2001)Google Scholar
  6. 6.
    Gardiner, C.W.: Handbook of stochastic methods. Springer, Berlin (1985)Google Scholar
  7. 7.
    Ghosh, R., Lerman, K.: Predicting influential users in online social networks. CoRR, abs/1005.4882 (2010)Google Scholar
  8. 8.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: WSDM, pp. 241–250 (2010)Google Scholar
  9. 9.
    Gruhl, D., Guha, R., Liben-Nowell, D., Tomkins, A.: Information diffusion through blogspace. In: WWW, pp. 491–501. ACM (2004)Google Scholar
  10. 10.
    Huang, Q., Yang, Q., Huang, J.Z., Ng, M.K.: Mining of web-page visiting patterns with continuous-time markov models. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 549–558. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD, pp. 137–146 (2003)Google Scholar
  12. 12.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW, pp. 591–600 (2010)Google Scholar
  13. 13.
    Liu, Y., Gao, B., Liu, T.Y., Zhang, Y., Ma, Z., He, S., Li, H.: Browserank: letting web users vote for page importance. In: SIGIR, pp. 451–458 (2008)Google Scholar
  14. 14.
    Mills, T.C.: Time series techniques for economists. Cambridge Univ. Pr. (1991)Google Scholar
  15. 15.
    Norris, J.R.: Markov chains. Number 2008. Cambridge University Press (1998)Google Scholar
  16. 16.
    Petrovic, S., Osborne, M., Lavrenko, V.: Rt to win! predicting message propagation in twitter. In: 5th ICWSM (2011)Google Scholar
  17. 17.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: SIGKDD, pp. 61–70 (2002)Google Scholar
  18. 18.
    Rogers, E.M.: Diffusion of Innovations, vol. 27. Free Press (2003)Google Scholar
  19. 19.
    Saito, K., Kimura, M., Ohara, K., Motoda, H.: Efficient estimation of cumulative influence for multiple activation information diffusion model with continuous time delay. In: Zhang, B.-T., Orgun, M.A. (eds.) PRICAI 2010. LNCS, vol. 6230, pp. 244–255. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  20. 20.
    Saito, K., Kimura, M., Ohara, K., Motoda, H.: Generative models of information diffusion with asynchronous timedelay. JMLR - Proceedings Track 13, 193–208 (2010)Google Scholar
  21. 21.
    Song, X., Chi, Y., Hino, K., Tseng, B.L.: Information flow modeling based on diffusion rate for prediction and ranking. In: WWW, pp. 191–200 (2007)Google Scholar
  22. 22.
    Song, X., Tseng, B.L., Lin, C.-Y., Sun, M.-T.: Personalized recommendation driven by information flow. In: SIGIR, pp. 509–516 (2006)Google Scholar
  23. 23.
    Tsur, O., Rappoport, A.: What’s in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. ACM (2012)Google Scholar
  24. 24.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: WSDM, pp. 261–270 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jingxuan Li
    • 1
  • Wei Peng
    • 2
  • Tao Li
    • 1
  • Tong Sun
    • 2
  1. 1.School of Computer ScienceFlorida International UniversityUSA
  2. 2.Xerox Innovation GroupXerox CorporationUSA

Personalised recommendations