Mining Communities in Directed Networks: A Game Theoretic Approach

Abstract

Detecting the communities in directed networks is a challenging task. Many of the existing community detection algorithm are designed to disclose the community structure for undirected networks. These algorithms can be applied to directed networks by transforming the directed networks to undirected. However, ignoring the direction of the links loses the information concealed along the link and end-up with imprecise community structure. In this paper, we retain the direction of the graph and propose a cooperative game in order to capture the interactions among the nodes of the network. We develop a greedy community detection algorithm to disclose the overlapping communities of the given directed network. Experimental evaluation on synthetic networks illustrates that the algorithm is able to disclose the correct number of communities with good community structure.