Journal of Combinatorial Optimization

, Volume 28, Issue 3, pp 513–528 | Cite as

A nature-inspired influence propagation model for the community expansion problem

  • Yuanjun Bi
  • Weili Wu
  • Yuqing Zhu
  • Lidan Fan
  • Ailian Wang


Influence propagation has been widely studied in social networks recently. Most of these existing work mainly focuses on the individual influence or the seed set influence. However, a large range of real world applications are related with the influence from communities. In this paper, we argue that the specific structure of community makes the influence propagation from a community different from previous influence propagation from an individual or a seed set. Inspired by the charged system in the physic, a new community influence propagation model is built, which provides a natural description about the process of influence propagation and explains why the influence makes communities expand. Based on this physical model, we define the community expansion problem. And two objective functions are proposed for choosing proper candidates to enlarge a community, taking into account the cost and benefit. Then a linear programming approach is designed to maximize those two objective functions. To validate our ideas and algorithm, we construct experiments on three real-world networks. The results demonstrate that our model and algorithm are effective in choosing proper candidates for expanding a community, comparing to other two algorithms.


Community expansion Physical model Social influence Linear programming 



This work was supported in part by the the US National Science Foundation (NSF) under Grant Nos. CNS-1016320, CCF-0829993 and CCF-0627233.


  1. Aggarwal CC (2011) An introduction to social network data analytics. Springer, BerlinCrossRefGoogle Scholar
  2. Aggarwal CC, Yu P (2005) Online analysis of community evolution in data streams. In Proceedings of the SIAM international conference on data mining (SDM 2005), pp 56–67Google Scholar
  3. Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans Knowl Discov Data (TKDD) 3(4):16Google Scholar
  4. Apostolopoulos T, Vlachos A (2010) Application of the firefly algorithm for solving the economic emissions load dispatch problem. Int J Combin 2011Google Scholar
  5. Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) 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, ACM, pp 44–54Google Scholar
  6. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008CrossRefGoogle Scholar
  7. Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, ACM. pp 554–560Google Scholar
  8. Domingos P, Richardson M (2001) Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 57–66Google Scholar
  9. Goyal A, Bonchi F, Lakshmanan LVS (2010) Learning influence probabilities in social networks. In Proceedings of the Third ACM international conference on web search and data mining, ACM. pp 241–250Google Scholar
  10. Hartline J, Mirrokni V, Sundararajan M (2008) Optimal marketing strategies over social networks. In Proceedings of the 17th international conference on world wide web, ACM, pp 189–198Google Scholar
  11. Halliday D, Resnick R, Walker J (2010) Fundamentals of physics extended. Wiley.comGoogle Scholar
  12. Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 137–146Google Scholar
  13. Leskovec J, Backstrom L, Kumar R, Tomkins A (2008) Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, ACM. pp 462–470Google Scholar
  14. Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N (2007) Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 420–429Google Scholar
  15. Lu Z, Fan L, Wu W, Thuraisingham B, Yang K (2014) Efficient influence spread estimation for influence maximization under the linear threshold model. Int J CombinGoogle Scholar
  16. Leskovec J, Lada A (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1(1):5CrossRefGoogle Scholar
  17. Nguyen NP, Dinh TN, Xuan Y, Thai MT (2011) Adaptive algorithms for detecting community structure in dynamic social networks. In Proceedings of the 30th IEEE international conference on computer communications (INFOCOM 2011), IEEE. pp 2282–2290Google Scholar
  18. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data Mining, ACM, pp 61–70Google Scholar
  19. Saito K, Nakano R, Kimura M (2008) Prediction of information diffusion probabilities for independent cascade model. In Knowledge-based intelligent information and engineering systems. Springer, Berlin, pp 67–75Google Scholar
  20. Sun T, Chen W, Liu Z, Wang Y, Sun X, Zhang M, Lin C-Y (2011) Participation maximization based on social influence in online discussion forums, In Proceedings of the fifth international AAAI conference on weblogs and social mediaGoogle Scholar
  21. Shimp TA (2013) Advertising promotion and other aspects of integrated marketing communications. Cengage LearnGoogle Scholar
  22. Wang C, Chen W, Wang Y (2012) Scalable influence maximization for independent cascade model in large-scale social networks. Data Min Knowl Discov 25(3):545–576CrossRefzbMATHMathSciNetGoogle Scholar
  23. Yanqing H, Chen H, Zhang P, Li M, Di Z, Fan Y (2008) Comparative definition of community and corresponding identifying algorithm. Phys Rev E 78(2):026121CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

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

Personalised recommendations