HGMAE: Hypergraph Masked Autoencoder

Hypergraphs have gained widespread exploration in various research domains due to their ability to model higher-order correlations among entities. Although existing studies on hypergraph learning have extended graph convolution to hypergraphs, they struggle to effectively extract features from unlabeled data. Recently, generative self-supervised learning, particularly masked autoencoder, has shown significant potential in handling unlabeled graph data, but applying it to hypergraphs still presents two key challenges: (1) How to accurately capture complex higher-order relationships of hypergraphs with generative learning; (2) How to maintain robustness when designing reconstruction strategies. To address these challenges, we propose theHyperGraphMaskedAutoEncoder (HGMAE), which leverages generative self-supervised learning techniques to learn from unlabeled data. Firstly, we employ attribute masking with dynamic linear masking rates and hyperedge masking to enable effective learning of hypergraphs. Subsequently, we adopt two distinct training strategies: node attribute reconstruction to capture various node attribute information and hypergraph-based equivalent bipartite graph reconstruction to capture higher-order semantic information in hypergraph structures. We conduct experiments on citation network classification and visual object recognition tasks, and the results demonstrate that the proposed HGMAE outperforms existing methods and baseline approaches on multiple benchmark datasets.