From Representation to Clusters: A Contrastive Learning Approach for Attributed Hypergraph Clustering
arXiv:2603.09370v1 Announce Type: new 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 cl
arXiv:2603.09370v1 Announce Type: new 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.
Executive Summary
This article proposes a novel contrastive learning approach for attributed hypergraph clustering, named CAHC. CAHC is an end-to-end method that simultaneously learns node embeddings and obtains clustering results. The method consists of two main steps: representation learning and cluster assignment learning. In the first step, a novel contrastive learning approach is employed to generate node embeddings by incorporating both node-level and hyperedge-level objectives. In the second step, joint embedding and clustering optimization is performed to refine the embeddings by clustering-oriented guidance. Experimental results demonstrate that CAHC outperforms baselines on eight datasets. This approach has the potential to improve the accuracy and efficiency of attributed hypergraph clustering and has significant implications for various applications, including social network analysis and recommendation systems.
Key Points
- ▸ CAHC is an end-to-end method for attributed hypergraph clustering
- ▸ CAHC uses a novel contrastive learning approach to generate node embeddings
- ▸ CAHC refines node embeddings using joint embedding and clustering optimization
Merits
Improved Accuracy
CAHC outperforms baselines on eight datasets, indicating its potential to improve the accuracy of attributed hypergraph clustering
Efficient Clustering
CAHC is an end-to-end method that simultaneously learns node embeddings and obtains clustering results, making it an efficient approach for attributed hypergraph clustering
Demerits
Limited Datasets
The article only reports results on eight datasets, which may limit the generalizability of the findings
Complexity
CAHC involves a novel contrastive learning approach and joint embedding and clustering optimization, which may increase the complexity of the method
Expert Commentary
The article proposes a novel approach for attributed hypergraph clustering, which has the potential to improve the accuracy and efficiency of this task. The use of a novel contrastive learning approach and joint embedding and clustering optimization is a significant contribution to the field. However, the limited number of datasets reported in the article may limit the generalizability of the findings. Additionally, the complexity of the method may make it challenging to implement in practice. Nevertheless, the development of CAHC is a significant step forward in the field of attributed hypergraph clustering and has significant implications for various applications.
Recommendations
- ✓ Future research should aim to evaluate CAHC on a larger and more diverse set of datasets
- ✓ The development of more efficient and scalable implementations of CAHC is necessary to make it practical for real-world applications