An Eigenvector-Based Kernel Clustering Approach to Detecting Communities in Complex Networks

Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 156)

Abstract

To detect communities in complex networks, we generalize the modularity density(D) to weighted variants and show how optimizing the weighted function(WD) can be formulated as a spectral clustering problem, as well as a weighted kernel k-means clustering problem.We also prove equivalence of the both clustering approaches based on WD in mathematics. Using the equivalence, we propose a new eigenvector-based kernel clustering algorithms to detecting communities in complex networks, called two-layer approach. Experimental results indicate that it have better performance comparing with either direct kernel k-means algorithm or direct spectral clustering algorithm in term of quality.

Keywords

Spectral Cluster Community Detection Kernel Matrix Normalize Mutual Information Community Versus 
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 GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of ComputerXi’an University of Science and TechnologyXi’anP.R. China
  2. 2.School of Computer Science and TechnologyXidian UniversityXi’anP.R. China

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