From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering

ArXi:2603.09370v1 Announce Type: new Contrastive learning has nstrated strong performance in attributed hypergraph clustering. Typically, existing methods based on contrastive learning first learn node embeddings and then apply clustering algorithms, such as k-means, to these embeddings to obtain the clustering results. However, these methods lack direct clustering supervision, risking the inclusion of clustering-irrelevant information in the learned graph.