Abstract
Modeling how information propagates in social networks driven by peer influence, is a fundamental research question towards understanding the structure and dynamics of these complex networks, as well as developing viral marketing applications. Existing literature studies influence at the level of individuals, mostly ignoring the existence of a community structure in which multiple nodes may exhibit a common influence pattern.
In this paper we introduce CSI, a model for analyzing information propagation and social influence at the granularity of communities. CSI builds over a novel propagation model that generalizes the classic Independent Cascade model to deal with groups of nodes (instead of single nodes) influence. Given a social network and a database of past information propagation, we propose a hierarchical approach to detect a set of communities and their reciprocal influence strength. CSI provides a higher level and more intuitive description of the influence dynamics, thus representing a powerful tool to summarize and investigate patterns of influence in large social networks. The evaluation on various datasets suggests the effectiveness of the proposed approach in modeling information propagation at the level of communities. It further enables to detect interesting patterns of influence, such as the communities that play a key role in the overall diffusion process, or that are likely to start information cascades.
Keywords
- Bayesian Information Crite
- Community Detection
- Minimum Description Length
- Social Graph
- Hierarchical Decomposition
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
References
Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: WSDM 2013 (2013)
Bonchi, F., Castillo, C., Donato, D., Gionis, A.: Taxonomy-driven lumping for sequence mining. Data Mining and Knowledge Discovery 19(2), 227–244 (2009)
Cover, T.M., Thomas, J.A.: Elements of information theory. Wiley-interscience (2012)
Domingos, P., Richardson, M.: Mining the network value of customers. In: KDD 2001 (2001)
Fortunato, S.: Community detection in graphs. Physics Reports 486(3), 75–174 (2010)
Garriga, G.C., Ukkonen, A., Mannila, H.: Feature selection in taxonomies with applications to paleontology. In: Boulicaut, J.-F., Berthold, M.R., Horváth, T. (eds.) DS 2008. LNCS (LNAI), vol. 5255, pp. 112–123. Springer, Heidelberg (2008)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: Learning influence probabilities in social networks. In: WSDM 2010 (2010)
Goyal, A., Bonchi, F., Lakshmanan, L.V.S.: A data-based approach to social influence maximization. PVLDB 5(1), 73–84 (2011)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD 2003 (2003)
Lavrač, N., Vavpetič, A., Soldatova, L., Trajkovski, I., Novak, P.K.: Using ontologies in semantic data mining with segs and g-segs. In: Elomaa, T., Hollmén, J., Mannila, H. (eds.) DS 2011. LNCS, vol. 6926, pp. 165–178. Springer, Heidelberg (2011)
Mathioudakis, M., Bonchi, F., Castillo, C., Gionis, A., Ukkonen, A.: Sparsification of influence networks. In: KDD 2011 (2011)
Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: SIGMOD 2008 (2008)
Navlakha, S., Schatz, M.C., Kingsford, C.: Revealing biological modules via graph summarization. Journal of Computational Biology 16(2), 253–264 (2009)
Rissanen, J.: A universal prior for integers and estimation by minimum description length. The Annals of Statistics, 416–431 (1983)
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds.) KES 2008, Part III. LNCS (LNAI), vol. 5179, pp. 67–75. Springer, Heidelberg (2008)
Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: KDD 2009 (2009)
Tian, Y., Hankins, R.A., Patel, J.M.: Efficient aggregation for graph summarization. In: SIGMOD 2008 (2008)
Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: WWW 2010 (2010)
Zhang, N., Tian, Y., Patel, J.M.: Discovery-driven graph summarization. In: ICDE 2010 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mehmood, Y., Barbieri, N., Bonchi, F., Ukkonen, A. (2013). CSI: Community-Level Social Influence Analysis. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40991-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-40991-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40990-5
Online ISBN: 978-3-642-40991-2
eBook Packages: Computer ScienceComputer Science (R0)