Comparative analysis for k-means algorithms in network community detection

Abstract

Detecting the community structure exhibited by real networks is a crucial step toward an understanding of complex systems beyond the local organization of their constituents. Many algorithms proposed so far, especially the group of methods in the k-means formulation, can lead to a high degree of efficiency and accuracy. Here we test three k-means methods, based on optimal prediction, diffusion distance and dissimilarity index, respectively, on two artificial networks, including the widely known ad hoc network with same community size and a recently introduced LFR benchmark graphs with heterogeneous distributions of degree and community size. All of them display an excellent performance, with the additional advantage of low computational complexity, which enables the analysis of large systems. Moreover, successful applications to several real world networks confirm the capability of the methods. © 2010 Springer-Verlag Berlin Heidelberg.