ADHERE: Adaptive Denoising for Robust Hypergraph Clustering

Hypergraph clustering aims to discover cluster structures by jointly exploiting node features and high-order relationships encoded by hyperedges. However, real-world hypergraphs inevitably contain feature and structure noise, which can propagate throughout the clustering process by degrading input reliability, distorting learned representations, and introducing unstable optimization signals. To address these issues, we propose an adaptive denoising-aware joint optimization framework for hypergraph clustering that tackles noise from input, representation, and optimization perspectives. Specifically, we design a differentiable denoising mechanism based on the reparameterization trick to adaptively select reliable node features and hyperedge incidences, thus generating two complementary denoised views. Then we perform cross-view contrastive learning in a high-dimensional space to learn consistent representations, while introducing pseudo-label-based self-supervised learning in a low-dimensional label space to explicitly reinforce cluster-level semantic structures. Furthermore, we adopt a parameter-pruning gradient update strategy to suppress unstable optimization signals during training. Extensive experiments on multiple benchmark datasets demonstrate the effectiveness and robustness of the proposed framework across several clustering metrics.