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Visual Clustering of Complex Network Based on Nonlinear Dimension Reduction

  • Jianyu Li
  • Shuzhong Yang
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 228)

Abstract

In this paper, we present a new visual clustering algorithm inspired by nonlinear dimension reduction technique: Isomap. The algorithm firstly defines a new graph distance between any two nodes in complex networks and then applies the distance matrix to Isomap and projects all nodes into a two dimensional plane, The experiments prove that the projected nodes emerge clear clustering property which is hidden in original complex networks and the distances between any two nodes reflect their close or distant relationships.

Key words

complex network visual clustering Isomap graph distance 

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Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Jianyu Li
    • 1
  • Shuzhong Yang
    • 2
  1. 1.School of Computer and SoftwareCommunication University of ChinaBeijingChina
  2. 2.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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