Edge pruning based community detection

Abstract

Given a network, community detection aims at finding all dense sub-graphs. Removing edges across different communities (border edges) is classic and effective. However, most of existing methods have high computing spends or suffer in the quality of resulting communities. In this paper, we propose a community detection algorithm: Edge Pruning (EP), with the fundamental idea of removing most possible border edges. To find out features of border edges, we first propose a method to measure the interplay between two nodes with a social tie, call Nodes Force Model. Second, since a node is influenced by all its connected nodes (neighbors), we discuss three possible situations of neighbors and compute their influence. Third, we study border edges, and find out their local features. With total influence and local features, we conclude a method to judge border edges. Edge Pruning has two advantages: (1) Detect communities with high quality (2) Low time complexity. Experimental results on real networks and synthetic networks demonstrate that Edge Pruning not only effectively detects communities with high quality, but also runs efficiently.