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Exploring Multiple Communities with Kernel-Based Link Analysis

  • Takahiko Ito
  • Masashi Shimbo
  • Daichi Mochihashi
  • Yuji Matsumoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

We discuss issues raised by applying von Neumann kernels to graphs with multiple communities. Depending on the parameter setting, Kandola et al.’s von Neumann kernels can identify not only nodes related to a given node but also the most important nodes in a graph. However, when von Neumann kernels are biased towards importance, top-ranked nodes are the important nodes in the dominant community of the graph irrespective of the communities where the target node belongs. To solve this “topic-drift” problem, we apply von Neumann kernels to the weighted graphs (community graph), which are derived from a generative model of links.

Keywords

Latent Dirichlet Allocation Citation Network Query Term Community Graph Discourse Processing 
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 2006

Authors and Affiliations

  • Takahiko Ito
    • 1
  • Masashi Shimbo
    • 1
  • Daichi Mochihashi
    • 2
  • Yuji Matsumoto
    • 1
  1. 1.Graduate School of Information Science Nara Institute of Science and Technology 
  2. 2.ATR Spoken Language Communication Research Laboratories 

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