A Feasible Community Detection Algorithm for Multilayer Networks

Abstract

As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.

Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

“…They use tensors to represent multilayer networks and slice bidirectionally to generate a single relational path matrix. Chen et al [23] fused the multilayer network into a weighted single-layer network by calculating the similarity between nodes and used an improved label propagation algorithm to obtain the community. However, these methods will cause the lack of network information in the process of integration and expansion, resulting in inaccurate community detection [24].…”