M^{2}HyCo: Multi-View Multi-Granularity Hypergraph Contrastive Learning

Recently, hypergraph contrastive learning (HCL) has received increasing attention due to its effectiveness in addressing high labeling costs and improving generalization. However, existing HCL methods often rely on random topology augmentation strategies and prioritize a single granularity, making it challenging to preserve critical structural information, high-order relationships, and a balance between global consistency and local feature dynamics during the representation process. To overcome these challenges, this paper proposes a multi-view multi-granularity hypergraph contrastive learning. Specifically, we first design a new sampling strategy that generates three complementary views to enhance hypergraph representation and improve diversity. In addition, we construct a hybrid attention encoder that integrates static homogeneity attention with dynamic self-attention to align global constraints with local adaptability. By jointly optimizing contrastive losses at both node and hyperedge levels, our framework effectively captures structural nuances and semantic correlations, significantly boosting both classification accuracy and clustering quality. This method is well-suited for scenarios with higher-order dependencies, such as session-based recommendation, social group interaction modeling, and biomedical multi-entity networks. Extensive experiments on multiple benchmark datasets demonstrate that M^{2}HyCo improves node classification accuracy by an average of 0.4 percentage points and clustering performance by 5.72 and 8.72 points in NMI and ARI, respectively, over recent strong baselines, especially on Cora-A and Pubmed, thereby evidencing its effectiveness under high-order structural dependencies.