Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

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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.