Detecting communities with multiple topics in attributed networks via self-supervised adaptive graph convolutional network

Abstract

Attributed networks which have topology information and attribute information simultaneously are very common in the real world, e.g., online social networks and co-authorship networks. Community detection in attribute networks is a very valuable research topic. Although there is already a lot of work focused on it, but most of existing approaches still face two key challenges: topology information and attribute information fusion, and multiple topics community detection. To effectively address these challenges, in this paper we devise an approach called SSAGCN, which adopts the self-supervised learning paradigm using the autoencoder architecture. Specifically, SSAGCN comprises three main parts: adaptive graph convolutional network (AGCN) encoder, modularity maximization and dual decoder. AGCN uses two GCNs with shared parameters and the attention mechanism to fuse topology information and attribute information automatically. To drive AGCN encoder to uncover community structure, we select the modularity maximization as the optimization objective. The dual decoder is applied to reconstruct both topology structure and network attributes. By introducing the joint training strategy, SSAGCN is able to discover multiple topics community through the end-to-end manner. Extensive experiments are conducted on nine benchmark attributed networks, and the results illustrate not only the superiority of SSAGCN over state-of-the-art approaches, but also its good ability of community-topic analysis. For the reproducibility, we release the source code at https://github.com/GDM-SCNU/SSAGCN.