  This paper targets at improving the generalizability of hypergraph neural
networks in the low-label regime, through applying the contrastive learning
approach from images/graphs (we refer to it as HyperGCL). We focus on the
following question: How to construct contrastive views for hypergraphs via
augmentations? We provide the solutions in two folds. First, guided by domain
knowledge, we fabricate two schemes to augment hyperedges with higher-order
relations encoded, and adopt three vertex augmentation strategies from
graph-structured data. Second, in search of more effective views in a
data-driven manner, we for the first time propose a hypergraph generative model
to generate augmented views, and then an end-to-end differentiable pipeline to
jointly learn hypergraph augmentations and model parameters. Our technical
innovations are reflected in designing both fabricated and generative
augmentations of hypergraphs. The experimental findings include: (i) Among
fabricated augmentations in HyperGCL, augmenting hyperedges provides the most
numerical gains, implying that higher-order information in structures is
usually more downstream-relevant; (ii) Generative augmentations do better in
preserving higher-order information to further benefit generalizability; (iii)
HyperGCL also boosts robustness and fairness in hypergraph representation
learning. Codes are released at https://github.com/weitianxin/HyperGCL.
