Contrastive Graph Clustering

Contrastive graph clustering aims to partition nodes in a graph into meaningful clusters by leveraging contrastive learning, a technique that learns representations by comparing similar and dissimilar data points. Recent research focuses on improving the reliability of contrastive learning in this context, exploring novel approaches to defining positive and negative sample pairs, and developing more effective data augmentation strategies or alternative similarity measures that incorporate both node attributes and structural information. These advancements enhance the accuracy and efficiency of graph clustering, with applications spanning diverse fields including social network analysis, anomaly detection, and bioinformatics.