Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. (arXiv:2210.03801v1 [cs.LG])

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 …

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