Adaptive Non-Overlapping Community Detection Based on Gravitational Field Stability in Social Networks

Abstract

Community structure is a common feature of social networks and many community discovery algorithms have emerged through the study of this feature. The gravitational field model is an effective method to realize community division. However, the current gravitational field model lacks a comprehensive consideration of field properties such as the internal stability of the gravitational field. Therefore, in this paper, we define and quantify the attributes of the gravitational field by taking advantage of the field’s strength in describing the joint action of groups. Then, we propose a social network gravitational field community detection model (GF-CDM). GF-CDM selects the field kernel node based on a random walk and then presents an adaptive expansion function of fusion field stability to divide the observable network into overlapping and non-overlapping clusters. The model was evaluated on four real network datasets and five artificial network datasets of different sizes. Experimental results show that our proposed model outperforms the other four benchmark algorithms in modularity, ARI index, and field average stability, which can improve the quality of cluster division.