Abstract
Identifying a set of nodes of size k in a large social network graph which maximizes the information flow or influence spread is a classical subset selection problem which is NP-Hard. Recently Charu Agarwal et al. in paper [10] proposed a stochastic information flow model and two algorithms namely, RankedReplace and BayesTraceBack to retrieve influential nodes. Among the two, RankedReplace algorithm gives better information spread, but does not scale well for large data sets. The main objective of paper is to speed up the RankedReplace algorithm without compromising on the information spread. To achieve this we are using the idea of degree discount heuristic from [8] and maximum degree heuristic. As shown by our experimental results, the proposed modifications reduce the amount of time significantly, maintaining the influence spread almost equal and even marginally better at times as compared to RankReplace algorithm. We have also proposed Willingness to send heuristic(WS) and an algorithm based on this WSRank for directed social network graphs.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proc. 7th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 57–66 (2001)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proc. 8th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 61–70 (2002)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. JACM 46(5), 604–632 (1999)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence in a social network. In: Proc. 9th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 137–146 (2003)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: KDD 2003: Proceedings of the Ninth ACMSIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM Press, New York, NY, USA (2003)
Kempe, D., Kleinberg, J.M., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.S.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2009)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large scale social networks. In: ACM KDD Conf., pp. 1029–1038 (2010)
Aggarwal, C.C., Khan, A., Yan, X.: On flow authority discovery in social networks. In: Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mustafa Faisan, M., Bhavani, S.D. (2014). Maximizing Information or Influence Spread Using Flow Authority Model in Social Networks. In: Natarajan, R. (eds) Distributed Computing and Internet Technology. ICDCIT 2014. Lecture Notes in Computer Science, vol 8337. Springer, Cham. https://doi.org/10.1007/978-3-319-04483-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-04483-5_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04482-8
Online ISBN: 978-3-319-04483-5
eBook Packages: Computer ScienceComputer Science (R0)