Hypergraph Structural Entropy Network for Attributed Hypergraph Clustering

Attributed hypergraph clustering leverages high-order relationship modeling to deliver discriminative vertex representations for data grouping. However, precise clustering is often hindered by the decoupling of the representation learning and grouping stages. Moreover, prevailing contrastive learning methods rely heavily on meticulously designed augmentations to achieve invariance, without explicitly guaranteeing a cluster-separable embedding space. To address these limitations, we propose the Hypergraph Structural Entropy Network (HSENet), a unified generative clustering framework that directly couples representation learning with precise data grouping. At its core, we formulate hypergraph structural entropy (HSE) to explicitly capture the hierarchical topology of hypergraphs, generalized for edge-dependent vertex weights (EDVW) and equipped with a continuous, almost-everywhere differentiable relaxation via the Lovász extension. Within a joint, end-to-end framework, HSENet couples two objectives: a masked generative self-supervised module to learn vertex semantics and structural topology, and an HSE-guided clustering assigner that optimizes a soft partitioning tree on an enhanced representation hypergraph. Extensive experiments on 8 benchmark datasets demonstrate that HSENet outperforms 10 competing methods, highlighting its clustering superiority.