A spectral algorithm of community identification

Abstract

A novel spectral algorithm utilizing multiple eigenvectors is proposed to identify the communities in networks based on the modularity Q. We investigate the reduced modularity on low-rank approximations of the original modularity matrix consisting of leading eigenvectors. By exploiting the rotational invariance of the reduced modularity, near-optimal partitions of the network can be found. This approach generalizes the conventional spectral network partitioning algorithms which usually use only one eigenvector, and promises better results because more spectral information is used. The algorithm shows excellent performance on various real-world and computer-generated benchmark networks, and outperforms the most known community detection methods. Copyright © EPLA, 2013.