Distance, dissimilarity index, and network community structure

Details Distance, dissimilarity index, and network community structure Distance, dissimilarity index, and network community structure

2003-02-11

arxiv.org/abs/physics/0302032v1

Physical Review E 67: 061901 (2003)

Physics

Biological Physics

10 pages, 7 figures, REVTeX4 format

Scientific paper

10.1103/PhysRevE.67.061901

We address the question of finding the community structure of a complex network. In an earlier effort [H. Zhou, {\em Phys. Rev. E} (2003)], the concept of network random walking is introduced and a distance measure defined. Here we calculate, based on this distance measure, the dissimilarity index between nearest-neighboring vertices of a network and design an algorithm to partition these vertices into communities that are hierarchically organized. Each community is characterized by an upper and a lower dissimilarity threshold. The algorithm is applied to several artificial and real-world networks, and excellent results are obtained. In the case of artificially generated random modular networks, this method outperforms the algorithm based on the concept of edge betweenness centrality. For yeast's protein-protein interaction network, we are able to identify many clusters that have well defined biological functions.

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