LICOD: A Leader-driven algorithm for community detection in complex networks

Abstract

Leader-driven community detection algorithms (LdCD hereafter) constitute a new trend in devising algorithms for community detection in large-scale complex networks. The basic idea is to identify some particular nodes in the target network, called leader nodes, around which local communities can be computed. Being based on local computations, they are particularly attractive to handle large-scale networks. In this paper, we describe a framework for implementing LdCD algorithms, called LICOD. We propose also a new way for evaluating performances of community detection algorithms. This consists on transforming data clustering problems into a community detection problems. External criteria for evaluating obtained clusters can then be used for comparing performances of different community detection approaches. Results we obtain show that our approach outperforms top state of the art algorithms for community detection in complex networks.