Hypergraphs are marked by complex topology, expressing higher-order
interactions among multiple nodes with hyperedges, and better capturing the
topology is essential for effective representation learning. Recent advances in
generative self-supervised learning (SSL) suggest that hypergraph neural
networks learned from generative self supervision have the potential to
effectively encode the complex hypergraph topology. Designing a generative SSL
strategy for hypergraphs, however, is not straightforward. Questions remain
with regard to its generative SSL task, connection to downstream tasks, and
empirical properties of learned representations. In light of the promises and
challenges, we propose a novel generative SSL strategy for hypergraphs. We
first formulate a generative SSL task on hypergraphs, hyperedge filling, and
highlight its theoretical connection to node classification. Based on the
generative SSL task, we propose a hypergraph SSL method, HypeBoy. HypeBoy
learns effective general-purpose hypergraph representations, outperforming 16
baseline methods across 11 benchmark datasets.

提出了一种针对超图的新型生成性自监督学习策略，通过超边填充的生成性自监督任务来学习复杂的超图拓扑，其中的方法 HypeBoy 在 11 个基准数据集上优于 16 种基线方法，学习了有效的通用超图表示。