  Hypergraphs play a pivotal role in the modelling of data featuring
higher-order relations involving more than two entities. Hypergraph neural
networks emerge as a powerful tool for processing hypergraph-structured data,
delivering remarkable performance across various tasks, e.g., hypergraph node
classification. However, these models struggle to capture global structural
information due to their reliance on local message passing. To address this
challenge, we propose a novel hypergraph learning framework, HyperGraph
Transformer (HyperGT). HyperGT uses a Transformer-based neural network
architecture to effectively consider global correlations among all nodes and
hyperedges. To incorporate local structural information, HyperGT has two
distinct designs: i) a positional encoding based on the hypergraph incidence
matrix, offering valuable insights into node-node and hyperedge-hyperedge
interactions; and ii) a hypergraph structure regularization in the loss
function, capturing connectivities between nodes and hyperedges. Through these
designs, HyperGT achieves comprehensive hypergraph representation learning by
effectively incorporating global interactions while preserving local
connectivity patterns. Extensive experiments conducted on real-world hypergraph
node classification tasks showcase that HyperGT consistently outperforms
existing methods, establishing new state-of-the-art benchmarks. Ablation
studies affirm the effectiveness of the individual designs of our model.
