  Hypergraphs can model higher-order relationships among data objects that are
found in applications such as social networks and bioinformatics. However,
recent studies on hypergraph learning that extend graph convolutional networks
to hypergraphs cannot learn effectively from features of unlabeled data. To
such learning, we propose a contrastive hypergraph neural network, CHGNN, that
exploits self-supervised contrastive learning techniques to learn from labeled
and unlabeled data. First, CHGNN includes an adaptive hypergraph view generator
that adopts an auto-augmentation strategy and learns a perturbed probability
distribution of minimal sufficient views. Second, CHGNN encompasses an improved
hypergraph encoder that considers hyperedge homogeneity to fuse information
effectively. Third, CHGNN is equipped with a joint loss function that combines
a similarity loss for the view generator, a node classification loss, and a
hyperedge homogeneity loss to inject supervision signals. It also includes
basic and cross-validation contrastive losses, associated with an enhanced
contrastive loss training process. Experimental results on nine real datasets
offer insight into the effectiveness of CHGNN, showing that it outperforms 13
competitors in terms of classification accuracy consistently.

Machine Learning;Artificial Intelligence