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

TL;DR

This work proposes a Contrastive learning approach for Attributed Hypergraph Clustering (CAHC), an end-to-end method that simultaneously learns node embeddings and obtains clustering results simultaneously.

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.

Figures (8)

• Figure 1: Comparison of contrastive learning-based hypergraph clustering methods and the proposed CAHC.
• Figure 2: Overview of CAHC. CAHC consists of two core steps: hypergraph representation learning and cluster assignment learning. Specifically, hypergraph representation learning optimizes $\mathcal{L}_{\text{hyper}} + \mathcal{L}_{\text{node}}$ to learn node embeddings from the inherent structural and attribute information of the data. Cluster assignment learning optimizes $\mathcal{L}_{\text{hyper}}$ + $\mathcal{L}_{\text{node}}$ + $\mathcal{L}_{\text{clu}}$ to achieve representations and clustering results.
• Figure 3: Impact of clustering guidance.
• Figure 4: Sensitivity analysis of $p_{\text{f}}$ and $p_{\text{m}}$.
• Figure 5: Sensitivity analysis of $D$.