The overlapping community discovery algorithm based on the local interaction model

Abstract

In social networks, the traditional locally optimized overlapping community detection algorithm has a free-rider problem in community extension, which mainly relies on the structure information of nodes but ignores the node attributes. Therefore, in this paper, we redefine community based on theoretical analysis and propose an overlapping community discovery algorithm based on the local interaction model. By fusing node attributes and structural information, we first proposed an improved density peak fast search method to obtain multiple core nodes in the community. Then, according to the interaction range and interaction mode of the core node, we established a local interaction model of the core node, which converts the interaction strength or the number of common attributes between nodes in the network into the change of the distance between nodes. Finally, according to the proposed improved clustering algorithm, we obtain the community where the core node is located and merge the communities with a high degree of overlap. The experimental results show that compared with other similar community discovery algorithms, the proposed method outperforms the state-of-the-art approaches for community detections.