Multilayer graph contrastive clustering network

Multilayer graphs have received significant research attention in numerous areas beacause of their high utility in modeling interdependent systems. However, clustering of the mul-tilayer graph, in which multiple networks divide the graph nodes into categories or com-munities, is still at a nascent stage. Existing graph-clustering methods are often restricted to exploiting the multiview attributes or multiple networks and ignore more complex and richer network frameworks. Thus, we propose a generic and an effective autoencoder framework for multilayer graph-clustering called a multilayer graph con-trastive clustering network (MGCCN). The MGCCN consists of three modules: (1) attention mechanism that is applied to better capture the relevance between nodes and their neigh-bors for better node embeddings. (2) A contrastive fusion strategy that efficiently explores the consistent information in different networks. (3) A self-supervised component that iteratively strengthens the node embedding and clustering. Extensive experiments on dif-ferent types of real-world graph data indicate that our proposed method outperforms other state-of-the-art techniques. (c) 2022 Elsevier Inc. All rights reserved.