Graph Based Clustering

Graph-based clustering leverages the relationships between data points, represented as a graph, to group similar items into clusters. Current research emphasizes developing robust algorithms that address challenges like handling noisy data, efficiently managing large datasets, and incorporating both feature information and graph structure effectively, often employing graph neural networks (GNNs) and techniques like contrastive learning and optimal transport. These advancements improve clustering accuracy and efficiency across diverse applications, including natural language processing, bioinformatics, and general data analysis, leading to more insightful interpretations of complex datasets.