Mining Diversity on Networks

  • Lu Liu
  • Feida Zhu
  • Chen Chen
  • Xifeng Yan
  • Jiawei Han
  • Philip S. Yu
  • Shiqiang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5981)


Despite the recent emergence of many large-scale networks in different application domains, an important measure that captures a participant’s diversity in the network has been largely neglected in previous studies. Namely, diversity characterizes how diverse a given node connects with its peers. In this paper, we give a comprehensive study of this concept. We first lay out two criteria that capture the semantic meaning of diversity, and then propose a compliant definition which is simple enough to embed the idea. An efficient top-k diversity ranking algorithm is developed for computation on dynamic networks. Experiments on both synthetic and real datasets give interesting results, where individual nodes identified with high diversities are intuitive.


Social Capital Short Path Dynamic Network Mining Diversity Betweenness Centrality 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Lu Liu
    • 1
  • Feida Zhu
    • 3
  • Chen Chen
    • 2
  • Xifeng Yan
    • 4
  • Jiawei Han
    • 2
  • Philip S. Yu
    • 5
  • Shiqiang Yang
    • 1
  1. 1.Tsinghua University 
  2. 2.University of IllinoisUrbana-Champaign
  3. 3.Singapore Management University 
  4. 4.University of CaliforniaSanta Barbara
  5. 5.University of IllinoisChicago

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