Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
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
Pinheiro CAR (2011) Social network analysis in telecommunications. Wiley, New York, p 4. ISBN 978-1-118-01094-5
Hartline JD, Mirrokni VS, Sundararajan M (2008) Optimal marketing strategies over social networks. In: Proc of the ACM WWW conf, pp 189–198
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
Domingos P, Richardson M (2001) Mining the network value of customers. In: Proc of the ACM SIGKDD conf, pp 57–66
Leskovec MJ, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web 1(1)
Brown J, Reinegen P (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362
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
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
Subramani MR, Rajagopalan B (2003) Knowledge-sharing and influence in online social networks via viral marketing. Commun ACM 46(12):300–307
Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Proc of the ACM SIGKDD conf, pp 61–70
Rogers EM (2003) Diffusion of innovations, 5th edn. Free Press, New York
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
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
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
Klimmt B, Yang Y (2004) Introducing the Enron corpus. In: CEAS conference
Acknowledgements
The work is partially supported by Natural Science Foundation of China (No. 11001237) and NUDT Preparing Research Project JC-02-01-04.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, X., Tao, H., Xie, Z. et al. Mining social networks using wave propagation. Comput Math Organ Theory 19, 569–579 (2013). https://doi.org/10.1007/s10588-012-9142-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10588-012-9142-x