Hypergraph Pre-training with Graph Neural Networks

Understand

Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.

• Among others, a major hurdle for effective hypergraph representation learning lies in the label scarcity of nodes and/or hyperedges.
• To address this issue, this paper presents an end-to-end, bi-level pre-training strategy with Graph Neural Networks for hypergraphs.
• The proposed framework named HyperGene bears three distinctive advantages.