Abstract
Ranking the centrality of a node within a graph is a fundamental problem in network analysis. Traditional centrality measures based on degree, betweenness, or closeness miss to capture the structural context of a node, which is caught by eigenvector centrality (EVC) measures. As a variant of EVC, PageRank is effective to model and measure the importance of web pages in the web graph, but it is problematic to apply it to other link-based ranking problems. In this paper, we propose a new influence propagation model to describe the propagation of predefined importance over individual nodes and groups accompanied with random walk paths, and we propose new IPRank algorithm for ranking both individuals and groups. We also allow users to define specific decay functions that provide flexibility to measure link-based centrality on different kinds of networks. We conducted testing using synthetic and real datasets, and experimental results show the effectiveness of our method.
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
Zhang, H., Smith, M., Giles, C.L., Yen, J., Foley, H.C.: Snakdd 2008 social network mining and analysis report. SIGKDD Explorations 10(2), 74–77 (2008)
Freeman, L.C.: Centrality in social networks: conceptual clarification. Social Networks 1, 215–239 (1978)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2(1), 113–120 (1972)
Newman, M.: The mathematics of networks. In: Blume, L., Durlauf, S. (eds.) The New Palgrave Encyclopedia of Economics, 2nd edn. Palgrave MacMillan, Basingstoke (2008), http://www-ersonal.umich.edu/~mejn/papers/palgrave.pdf
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab (1999)
Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)
Everett, M.G., Borgatti, S.P.: Extending centrality. In: Wasserman, S., Faust, K. (eds.) Social network analysis: methods and applications, pp. 58–63. Cambridge University Press, Cambridge (1994)
Motwani, R., Raghavan, P.: Randomized Algorithms. Cambridge University Press, Cambridge (1995)
Valente, T.: Network Models of the Diffusion of Innovations. Hampton Press, New Jersey (1995)
Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.O.: Combating web spam with trustrank. In: VLDB, pp. 576–587 (2004)
Sarkar, P., Moore, A.W.: Fast dynamic reranking in large graphs. In: WWW, pp. 31–40 (2009)
Centrality in Wikipedia, http://en.wikipedia.org/wiki/Centrality
Dangalchev, C.: Mining frequent cross-graph quasi-cliques. Physica A: Statistical Mechanics and its Applications 365(2), 556–564 (2006)
Tong, H., Papadimitriou, S., Yu, P.S., Faloutsos, C.: Proximity tracking on time-evolving bipartite graphs. In: SDM, pp. 704–715 (2008)
Guha, R.V., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: WWW, pp. 403–412 (2004)
Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW, pp. 517–526 (2002)
Lin, Z., Lyu, M.R., King, I.: Pagesim: a novel link-based measure of web page aimilarity. In: WWW, pp. 1019–1020 (2006)
Baeza-Yates, R.A., Boldi, P., Castillo, C.: Generalizing pagerank: damping functions for link-based ranking algorithms. In: SIGIR, pp. 308–315 (2006)
Jiang, D., Pei, J.: Mining frequent cross-graph quasi-cliques. TKDDÂ 2(4) (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, P., Yu, J.X., Liu, H., He, J., Du, X. (2011). Ranking Individuals and Groups by Influence Propagation. In: Huang, J.Z., Cao, L., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science(), vol 6635. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20847-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-20847-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20846-1
Online ISBN: 978-3-642-20847-8
eBook Packages: Computer ScienceComputer Science (R0)