FOX: Fast Overlapping Community Detection Algorithm in Big Weighted Networks

Abstract

Community detection is a hot topic for researchers in the fields of graph theory, social networks, and biological networks. Generally speaking, a community refers to a group of densely linked nodes in the network. Nodes usually have more than one community label, indicating their multiple roles or functions in the network. Unfortunately, existing solutions aiming at overlapping community detection are not capable of scaling to large-scale networks with millions of nodes and edges. In this article, we propose a fastoverlapping-community-detection algorithm—FOX. In the experiment on a network with 3.9 millions nodes and 20 millions edges, the detection finishes in 41 min and provides the most qualified results. The secondfastest algorithm, however, takes almost five times longer to run. As for another network with 22 millions nodes and 127 millions edges, our algorithm is the only one that can provide an overlapping community detection result and it only takes 533 min. Our algorithm is a typical heuristic algorithm, measuring the closeness of a node to a community by counting the number of triangles formed by the node and two other nodes in the community. We also extend the exploitation of triangle to open-triangle, which enlarges the scale of the detected communities.