On community detection in very large networks

Abstract

Community detection or graph clustering is an important problem in the analysis of computer networks, social networks, biological networks and many other natural and artificial networks. These networks are in general very large and, thus, finding hidden structures and functional modules is a very hard task. In this paper we propose new data structures and a new implementation of a well known agglomerative greedy algorithm to find community structure in large networks, the CNM algorithm. The experimental results show that the improved data structures speedup the method by a large factor, for large networks, making it competitive with other state of the art algorithms. © 2011 Springer-Verlag.