Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

Abstract

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.

General Community Detection in Attributed Networks with Consistent-Module Constrained Nonnegative Matrix Factorization

“…For directed and undirected networks classified according to the directionality of edges, Kuncheva et al [32] proposed SNMF and ANMF to extract the intrinsic community structures, respectively. Considering the modularity information of the network, Jia et al [28] presented a trifactor NMF model that combines the modularity information as a regularization term. To further capture the complex underlying network structure effectively and preserve the global and local structures, Li et al [33] proposed a multilayer model based on NMF, which consists of an encoder module and a decoder module.…”

“…Among them, nonnegative matrix factorization (NMF) based methods have attracted much interest due to their good performance and strong interpretability. For example, Jia et al [28] developed a modularized trifactor matrix factorization model Mtrinmf to exploit the topological and the modularity information of the network. Zhang et al [3] used the NMF method to improve density peak clustering in community detection.…”

A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

“…Therefore, using an approach based on equality and similar affiliation of user nodes may produce inaccurate segmentation results. To address these issues, several recent studies have developed user segmentation models to capture the diversity and heterogeneity of OICs [18,20,21]. Multiple entities and edge types in heterogeneous OICs provide a large amount of information that can be effectively used to mitigate sparsity effects and improve decision efficiency.…”