  Multilayer graph has garnered plenty of research attention in many areas due
to their high utility in modeling interdependent systems. However, clustering
of multilayer graph, which aims at dividing the graph nodes into categories or
communities, is still at a nascent stage. Existing methods are often limited to
exploiting the multiview attributes or multiple networks and ignoring more
complex and richer network frameworks. To this end, we propose a generic and
effective autoencoder framework for multilayer graph clustering named
Multilayer Graph Contrastive Clustering Network (MGCCN). MGCCN consists of
three modules: (1)Attention mechanism is applied to better capture the
relevance between nodes and neighbors for better node embeddings. (2)To better
explore the consistent information in different networks, a contrastive fusion
strategy is introduced. (3)MGCCN employs a self-supervised component that
iteratively strengthens the node embedding and clustering. Extensive
experiments on different types of real-world graph data indicate that our
proposed method outperforms state-of-the-art techniques.
