Gravitational community detection by predicting diameter

Abstract

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.