Community Expansion in Social Network

  • Yuanjun Bi
  • Weili Wu
  • Li Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7825)


While most existing work about community focus on the community structure and the tendency of one individual joining a community; equally important is to understand social influence from community and to find strategies of attracting new members to join the community, which may benefit a range of applications. In this paper, we formally define the problem of community expansion in social network, which is under the marketing promotional activities scenario. We propose three models, Adopter Model, Benefit Model and Combine Model, to present different promotion strategies over time, taking into consideration the community structure characters. Specifically, Adopter Model is based on the factors that can make an individual come into a community. Benefit Model considers the factors that attract more new members. Combine Model aims to find a balance between Adopter Model and Benefit Model. Then a greedy algorithm ETC is developed for expanding a community over time. Our results from extensive simulation on several real-world networks demonstrate that our Combine Model performs effectively and outperforms other algorithms.


community expansion community strategy social network 


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  1. 1.
  2. 2.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. In: PNAS (2002)Google Scholar
  3. 3.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. (2004)Google Scholar
  4. 4.
    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
  5. 5.
    Backstrom, L., Huttenlocher, D., Kleinberg, J., Lan, X.: Group formation in large social networks: membership, growth, and evolution. In: KDD (2006)Google Scholar
  6. 6.
    Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: KDD (2006)Google Scholar
  7. 7.
    Burt, R.: Structural Holes: The Social Structure of Competition. Harvard University Press (1992)Google Scholar
  8. 8.
    McKenzie Mohr, D., Smith, W.: Fostering Sustainable Behavior: An Introduction to Community Based Social Marketing. New Society Publishers (1971)Google Scholar
  9. 9.
    Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web (2007)Google Scholar
  10. 10.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD (2001)Google Scholar
  11. 11.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: KDD (2002)Google Scholar
  12. 12.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD (2003)Google Scholar
  13. 13.
    Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale soical networks. Data Mining and Knowledge Discovery (2012)Google Scholar
  14. 14.
    Tang, S., Yuan, J., Mao, X., Li, X., Chen, W., Dai, G.: Relationship classification in large scale online social networks and its impact on information propagation. In: INFOCOM (2011)Google Scholar
  15. 15.
    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
  16. 16.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: WSDM (2010)Google Scholar
  17. 17.
    Milgram, S.: The small world problem. Psychology Today (1967)Google Scholar
  18. 18.
    Shimp, T.A.: Advertising promotion: Supplemental aspects of integrated marketing communications. South-Western College Pub. (2002)Google Scholar
  19. 19.
    Blondel, V.D., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. (2008)Google Scholar
  20. 20.
    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
  21. 21.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuanjun Bi
    • 1
  • Weili Wu
    • 1
    • 2
  • Li Wang
    • 2
  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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