Deep Graph Clustering

Deep graph clustering aims to partition nodes in a graph into meaningful clusters using deep learning techniques, primarily focusing on learning effective node representations for improved clustering accuracy. Current research emphasizes developing novel graph neural network (GNN) architectures, often incorporating contrastive learning or mutual information maximization, to address challenges like representation collapse and the need for pre-defined cluster numbers. These advancements are improving the performance and scalability of graph clustering, with significant implications for various fields including community detection in social networks, bioinformatics, and time series analysis.