Hypergraph Clustering has gained significant attention due to its capability of capturing high order structural information. Among different approaches, contrastive learning-based methods leverage self-supervised learning and data augmentation, exhibiting impressive performance. However, most of them come with the following limitations: 1) Augmentation strategies like feature dropout can potentially disrupt the intrinsic clustering structure of hypergraphs. 2) High computational demands hinder their real-world application. To address the above issues, we propose a simple yet effective Hypergraph Clustering Network framework (HCN). Specifically, HCN replaces the hypergraph convolution operation with smoothing preprocessing, which avoids high computational complexity. Besides, to retain intrinsic structure, it develops two key modules: the self-diagonal consistency module and the structure alignment mod ule. They respectively align the similarity matrix with the identity matrix and the structural affinity matrix, which ensures intra-cluster compact ness and inter-cluster separability. Extensive experiments on five benchmark datasets demonstrate HCN’s superiority over state-of-the-art methods.
Wang et al. (Mon,) studied this question.