Research on Overlapping Community Detection Based on Density Peak Clustering in Heterogeneous Networks

Abstract

To solve the problem of finding overlapping communities in heterogeneous networks, this paper proposes a model for finding overlapping communities in heterogeneous networks based on density peak clustering. The model improves the heterogeneous graph attention network, which fully mines for the important information of nodes and meta-paths in information representation from the node level and the semantic level attention mechanisms and performs hierarchical aggregation to obtain heterogeneous graph embedding vectors. It combines the heterogeneous graph attention network with density peak clustering and calculates node density and relative distance through the heterogeneous graph embedding vector to divide community center nodes. Then it also uses the weight information of the node-level attention mechanism to generate a community membership matrix. The experimental results on real datasets show that the model can utilize the diversity of heterogeneous network node information to discover overlapping communities with good stability and accuracy.