Graph Clustering

Graph clustering aims to partition nodes in a graph into distinct groups based on their connectivity and attributes, facilitating analysis of complex relationships within networks. Current research emphasizes developing robust and scalable algorithms, often leveraging graph neural networks (GNNs) and contrastive learning techniques, to address challenges posed by large graphs, noisy data, and varying levels of homophily (similarity between connected nodes). These advancements improve clustering accuracy and efficiency across diverse applications, including social network analysis, bioinformatics, and image processing, leading to more insightful interpretations of complex datasets.