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)


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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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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