Abstract
Online social network today is an effective media to share and disperse tons of information, especially for advertizing and marketing. However, with limited budgets, commercial companies make hard efforts to determine a set of source persons who can highly diffuse information of their products, implying that more benefits will be received. In this paper, we propose an algorithm, called community centrality-based greedy algorithm, for the problem of finding top-k influencers in social networks. The algorithm is composed of four main processes. First, a social network is partitioned into communities using the Markov clustering algorithm. Second, nodes with highest centrality values are extracted from each community. Third, some communities are combined; and last, top-k influencers are determined from a set of highest centrality nodes based on the independent cascade model. We conduct experiments on a publicly available Higgs Twitter dataset. Experimental results show that the proposed algorithm executes much faster than the state-of-the-art greedy one, while still maximized nearly the same influence spread.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208 (2009)
Chen, W., Yuan, Y., Zhang, L.: Scalable influence maximization in social networks under the linear threshold model. In: Proceedings of the IEEE International Conference on Data Mining, pp. 88–97 (2010)
Chen, Y.C., Zhu, W.Y., Peng, W.C., Lee, W.C., Lee, S.Y.: CIM: community-based influence maximization in social networks. ACM Trans. Intell. Syst. Technol. 5(2), 25:1–25:31 (2014)
De Domenico, M., Lima, A., Mougel, P., Musolesi, M.: The anatomy of a scientific rumor. Scientific Reports 3(2980) (2013)
Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)
Galstyan, A., Musoyan, V.L., Cohen, P.R.: Maximizing influence propagation in networks with community structure. Phys. Rev. E. 79(5), 056102 (2009)
Goyal, A., Lu, W., Lakshmanan, L.V.S.: CELF++: Optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference on World Wide Web (Companion Volume), pp. 47–48 (2011)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (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)
Kim, C., Lee, S., Park, S., Lee, S.: Influence maximization algorithm using markov clustering. In: Hong, B., Meng, X., Chen, L., Winiwarter, W., Song, W. (eds.) DASFAA Workshops 2013. LNCS, vol. 7827, pp. 112–126. Springer, Heidelberg (2013)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429 (2007)
Mehmood, Y., Barbieri, N., Bonchi, F., Ukkonen, A.: CSI: community-level social influence analysis. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part II. LNCS, vol. 8189, pp. 48–63. Springer, Heidelberg (2013)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford Digital Libraries (1999)
Van Dongen, S.: Graph clustering via a discrete uncoupling process. SIAM J. Matrix Anal. Appl. 30(1), 121–141 (2008)
Wang, Y., Cong, G., Song, G., Xie, K.: Community-based greedy algorithm for mining top-K influential nodes in mobile social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1039–1048 (2008)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge University Press, New York (1994)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Manaskasemsak, B., Dejkajonwuth, N., Rungsawang, A. (2016). Community Centrality-Based Greedy Approach for Identifying Top-K Influencers in Social Networks. In: Vinh, P., Alagar, V. (eds) Context-Aware Systems and Applications. ICCASA 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-29236-6_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-29236-6_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29235-9
Online ISBN: 978-3-319-29236-6
eBook Packages: Computer ScienceComputer Science (R0)