Community Expansion Model Based on Charged System Theory

  • Yuanjun Bi
  • Weili Wu
  • Ailian Wang
  • Lidan Fan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7936)

Abstract

Recently, the phenomenon of influence propagation becomes a hot topic in social networks. However, few existing influence models study the influence from communities, which has a large range of applications. In this paper, we use the charged system model to represent the social influence. This model provides a natural description about the behaviors of influence and explains why the influence makes communities expand. Based on this physical model, we propose two objective functions for choosing proper candidates to enlarge a community, considering of the cost and benefit issue. Then a linear programming approach is given to maximize those two objective functions. We validate our ideas and algorithm using two real-world networks. The results demonstrate that our model can choose excellent propagation candidates for a specific community, comparing to other two algorithms.

Keywords

community expansion physical model social influence linear programming 

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References

  1. 1.
    Aggarwal, C.C.: Social Network Data Analytics. Springer (2011)Google Scholar
  2. 2.
    Aggarwal, C.C., Yu, P.S.: Online analysis of community evolution in data streams. In: SDM (2005)Google Scholar
  3. 3.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)Google Scholar
  4. 4.
    Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale soical networks. In: Data Mining and Knowledge Discovery (2012)Google Scholar
  5. 5.
    Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Sun, T., Chen, W., Liu, Z., Wang, Y., Sun, X., Zhang, M., Lin, C.: Participation maximization based on social influence in online discussion forums. In: ICWSM (2011)Google Scholar
  7. 7.
    Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A.: Microscopic evolution of social networks. In: KDD (2008)Google Scholar
  8. 8.
    Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM (2011)Google Scholar
  9. 9.
    Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: KDD (2006)Google Scholar
  10. 10.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: WSDM (2010)Google Scholar
  11. 11.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD (2001)Google Scholar
  12. 12.
    Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web (2007)Google Scholar
  13. 13.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)Google Scholar
  14. 14.
    Shimp, T.A.: Advertising promotion: Supplemental aspects of integrated marketing communications. South-Western College Pub. (2002)Google Scholar
  15. 15.
    Hartline, J., Mirrokni, V., Sundararajan, M.: Optimal marketing strategies over social networks. In: WWW (2008)Google Scholar
  16. 16.
    Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics, 8th edn. Wiley (2007)Google Scholar
  17. 17.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)Google Scholar
  18. 18.
    Hu, Y., Chen, H., Zhang, P., Di, Z., Li, M., Fan, Y.: Comparative definition of community and corresponding identifying algorithm. Phys. Rev. (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuanjun Bi
    • 1
  • Weili Wu
    • 1
    • 2
  • Ailian Wang
    • 2
  • Lidan Fan
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas at DallasRichardsonUSA
  2. 2.College of Computer Science and TechnologyTaiYuan University of TechnologyTaiyuanChina

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