How to detect communities in large networks

Abstract

Community detection is a very popular research topic in network science nowadays. Various categories of community detection algorithms have been proposed, such as graph partitioning, hierarchical clustering, partitional clustering. Due to the high computational complexity of those algorithms, it is impossible to apply those algorithms to large networks. In order to solve the problem, Blondel introduced a new greedy approach named lovian to apply to large networks. But the remained problem lies in that the community detection result is not unstable due to the random choice of seed nodes. In this paper, we present a new modularity optimization method, LPR, for community detection, which chooses the node in order of the PageRank value rather than randomly. The experiments are executed by using medium-sized networks and large networks respectively for community detection. Comparing with lovian algorithm, the LPR method achieves better performance and higher computational efficiency, indicating the order of choosing seed nodes greatly influences the efficiency of community detection. In addition, we can get the importance values of nodes which not only is part of our algorithm, but also can be used to detect the community kernel in the network independently.