Ranking Individuals and Groups by Influence Propagation

  • Pei Li
  • Jeffrey Xu Yu
  • Hongyan Liu
  • Jun He
  • Xiaoyong Du
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6635)

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.

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References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Freeman, L.C.: Centrality in social networks: conceptual clarification. Social Networks 1, 215–239 (1978)CrossRefGoogle Scholar
  3. 3.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2(1), 113–120 (1972)CrossRefGoogle Scholar
  4. 4.
    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 Google Scholar
  5. 5.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical Report 1999-66, Stanford InfoLab (1999)Google Scholar
  6. 6.
    Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Motwani, R., Raghavan, P.: Randomized Algorithms. Cambridge University Press, Cambridge (1995)CrossRefMATHGoogle Scholar
  9. 9.
    Valente, T.: Network Models of the Diffusion of Innovations. Hampton Press, New Jersey (1995)Google Scholar
  10. 10.
    Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.O.: Combating web spam with trustrank. In: VLDB, pp. 576–587 (2004)Google Scholar
  11. 11.
    Sarkar, P., Moore, A.W.: Fast dynamic reranking in large graphs. In: WWW, pp. 31–40 (2009)Google Scholar
  12. 12.
    Centrality in Wikipedia, http://en.wikipedia.org/wiki/Centrality
  13. 13.
    Dangalchev, C.: Mining frequent cross-graph quasi-cliques. Physica A: Statistical Mechanics and its Applications 365(2), 556–564 (2006)CrossRefGoogle Scholar
  14. 14.
    Tong, H., Papadimitriou, S., Yu, P.S., Faloutsos, C.: Proximity tracking on time-evolving bipartite graphs. In: SDM, pp. 704–715 (2008)Google Scholar
  15. 15.
    Guha, R.V., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: WWW, pp. 403–412 (2004)Google Scholar
  16. 16.
    Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW, pp. 517–526 (2002)Google Scholar
  17. 17.
    Lin, Z., Lyu, M.R., King, I.: Pagesim: a novel link-based measure of web page aimilarity. In: WWW, pp. 1019–1020 (2006)Google Scholar
  18. 18.
    Baeza-Yates, R.A., Boldi, P., Castillo, C.: Generalizing pagerank: damping functions for link-based ranking algorithms. In: SIGIR, pp. 308–315 (2006)Google Scholar
  19. 19.
    Jiang, D., Pei, J.: Mining frequent cross-graph quasi-cliques. TKDD 2(4) (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pei Li
    • 1
  • Jeffrey Xu Yu
    • 2
  • Hongyan Liu
    • 3
  • Jun He
    • 1
  • Xiaoyong Du
    • 1
  1. 1.Renmin University of ChinaBeijingChina
  2. 2.The Chinese University of Hong KongHong Kong, China
  3. 3.Tsinghua UniversityBeijingChina

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