HypeBoy: Generative Self-Supervised Representation Learning on
  Hypergraphs
Sunwoo Kim Shinhwan Kang Fanchen Bu Soo Yong Lee Jaemin Yoo Kijung Shin
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.