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

• URL: http://arxiv.org/abs/2603.09370v1
• Date: Tue, 10 Mar 2026 08:46:27 GMT
• Title: From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
• Abstract summary: We propose a Contrastive learning approach for Attributed Hypergraph Clustering (CAHC)<n>CAHC simultaneously learns node embeddings and obtains clustering results.<n>Extensive experimental results demonstrate that CAHC outperforms baselines on eight datasets.
• Score: 7.7028814149787825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning has demonstrated 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.To this end, we propose a Contrastive learning approach for Attributed Hypergraph Clustering (CAHC), an end-to-end method that simultaneously learns node embeddings and obtains clustering results. CAHC consists of two main steps: representation learning and cluster assignment learning. The former employs a novel contrastive learning approach that incorporates both node-level and hyperedge-level objectives to generate node embeddings.The latter joint embedding and clustering optimization to refine these embeddings by clustering-oriented guidance and obtains clustering results simultaneously.Extensive experimental results demonstrate that CAHC outperforms baselines on eight datasets.

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