Bibliometric approach to community discovery

Abstract

Recent research suggests that most of the real-world random networks organize themselves into communities. Communities are formed by subsets of nodes in a graph, which are closely related. Extracting these communities would lead to a better understanding of such networks. In this paper we propose a novel approach to discover communities using bibliographic metrics, and test the proposed algorithm on real-world networks as well as with computer-generated models with known community structure.