Mining social networks using wave propagation

  • Xiaojie Wang
  • Hong Tao
  • Zheng Xie
  • Dongyun Yi


With the development of modern technology(communication, transportation, etc.), many new social networks have formed and influenced our life. The research of mining these new social networks has been used in many aspects. But compared with traditional networks, these new social networks are usually very large. Due to the complexity of the latter, few model can be adapted to mine them effectively. In this paper, we try to mine these new social networks using Wave Propagation process and mainly discuss two applications of our model, solving Message Broadcasting problem and Rumor Spreading problem. Our model has the following advantages: (1) We can simulate the real networks message transmitting process in time since we include a time factor in our model. (2) Our Message Broadcasting algorithm can mine the underlying relationship of real networks and represent some clustering properties. (3) We also provide an algorithm to detect social network and find the rumor makers. Complexity analysis shows our algorithms are scalable for large social network and stable analysis proofs our algorithms are stable.


Social network Wave propagation Message broadcasting Rumor spreading 



The work is partially supported by Natural Science Foundation of China (No. 11001237) and NUDT Preparing Research Project JC-02-01-04.


  1. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: Proc of the seventh international conference on the world wide web, pp 107–117 Google Scholar
  2. Pinheiro CAR (2011) Social network analysis in telecommunications. Wiley, New York, p 4. ISBN 978-1-118-01094-5 Google Scholar
  3. Hartline JD, Mirrokni VS, Sundararajan M (2008) Optimal marketing strategies over social networks. In: Proc of the ACM WWW conf, pp 189–198 Google Scholar
  4. Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. In: Proc of the ACM SIGKDD conf, pp 137–146 Google Scholar
  5. Domingos P, Richardson M (2001) Mining the network value of customers. In: Proc of the ACM SIGKDD conf, pp 57–66 Google Scholar
  6. Leskovec MJ, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 1(1) Google Scholar
  7. Brown J, Reinegen P (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362 CrossRefGoogle Scholar
  8. Goldenberg J, Libai B, Muller E (2001) Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark Lett 12(3):211–223 CrossRefGoogle Scholar
  9. Mahajan V, Muller E, Bass F (1999) New product diffusion models in marketing: a review and directions for research. J Mark 54(1):1–26 Google Scholar
  10. Subramani MR, Rajagopalan B (2003) Knowledge-sharing and influence in online social networks via viral marketing. Commun ACM 46(12):300–307 CrossRefGoogle Scholar
  11. Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proc of the ACM SIGKDD conf, pp 61–70 Google Scholar
  12. Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York Google Scholar
  13. Ma H, Yang H, Lyu MR, King I (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: CIKM’08, October 26–30 Google Scholar
  14. Fountoulakis N, Panagiotou K (2010) Rumor spreading on random regular graphs and expanders. In: 14th inter workshop on randomization and comput (RANDOM). LNCS, vol 6302, pp 560–573 Google Scholar
  15. Leskovec J, Lang K, Dasgupta A, Mahoney M (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123 CrossRefGoogle Scholar
  16. Klimmt B, Yang Y (2004) Introducing the Enron corpus. In: CEAS conference Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Mathematics and System Science, College of ScienceNational University of Defense TechnologyChangshaChina

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