Multilayer Graph Contrastive Clustering Network
This addresses a nascent problem in graph analysis for researchers and practitioners dealing with interdependent systems, offering an incremental improvement over existing methods.
The paper tackles the problem of clustering multilayer graphs, which is underdeveloped, by proposing a generic autoencoder framework called MGCCN that uses attention, contrastive fusion, and self-supervised components to improve node embeddings and clustering, achieving state-of-the-art performance in experiments on real-world data.
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.