Computer Science > Machine Learning
arXiv:2603.09370 (cs)
[Submitted on 10 Mar 2026]
Title:From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
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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 this http URL, these methods lack direct clustering supervision, risking the inclusion of clustering-irrelevant information in the learned this http URL 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 this http URL latter joint embedding and clustering optimization to refine these embeddings by clustering-oriented guidance and obtains clustering results this http URL experimental results demonstrate that CAHC outperforms baselines on eight datasets.
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| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09370 [cs.LG] |
| (or arXiv:2603.09370v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09370
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View a PDF of the paper titled From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering, by Li Ni and 3 other authors
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