Fast detection of size-constrained communities in large networks

Abstract

The community detection in networks is a prominent task in the graph data mining, because of the rapid emergence of the graph data; e.g., information networks or social networks. In this paper, we propose a new algorithm for detecting communities in networks. Our approach differs from others in the ability of constraining the size of communities being generated, a property important for a class of applications. In addition, the algorithm is greedy in nature and belongs to a small family of community detection algorithms with the pseudo-linear time complexity, making it applicable also to large networks. The algorithm is able to detect small-sized clusters independently of the network size. It can be viewed as complementary approach to methods optimizing modularity, which tend to increase the size of generated communities with the increase of the network size. Extensive evaluation of the algorithm on synthetic benchmark graphs for community detection showed that the proposed approach is very competitive with state-of-the-art methods, outperforming other approaches in some of the settings. © 2010 Springer-Verlag Berlin Heidelberg.