Multilayer Graph Contrastive Clustering Network

TL;DR

This paper introduces MGCCN, a novel autoencoder framework for multilayer graph clustering that leverages attention, contrastive fusion, and self-supervision to improve community detection in complex networks.

Contribution

The paper proposes a new multilayer graph clustering method combining attention, contrastive fusion, and self-supervised learning, addressing limitations of existing approaches.

Findings

Outperforms state-of-the-art methods on real-world datasets.

Effectively captures node relevance with attention mechanism.

Enhances clustering accuracy through contrastive fusion and self-supervision.

Abstract

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.