Multi View Graph Clustering

Multi-view graph clustering aims to group data points from multiple interconnected data sources (views) by leveraging their combined graph structures. Current research emphasizes developing efficient algorithms that address the challenges posed by heterophilous graphs (where connected nodes don't always belong to the same cluster) and incomplete or unpaired data across views, often employing techniques like adaptive graph filtering, stochastic block models, and anchor-based methods for improved scalability and accuracy. These advancements are significant for various applications requiring integrated analysis of complex, multi-faceted data, such as social network analysis, bioinformatics, and recommendation systems.