  Hypergraphs, with their capacity to depict high-order relationships, have
emerged as a significant extension of traditional graphs. Although Graph Neural
Networks (GNNs) have remarkable performance in graph representation learning,
their extension to hypergraphs encounters challenges due to their intricate
structures. Furthermore, current hypergraph transformers, a special variant of
GNN, utilize semantic feature-based self-attention, ignoring topological
attributes of nodes and hyperedges. To address these challenges, we propose a
Topology-guided Hypergraph Transformer Network (THTN). In this model, we first
formulate a hypergraph from a graph while retaining its structural essence to
learn higher-order relations within the graph. Then, we design a simple yet
effective structural and spatial encoding module to incorporate the topological
and spatial information of the nodes into their representation. Further, we
present a structure-aware self-attention mechanism that discovers the important
nodes and hyperedges from both semantic and structural viewpoints. By
leveraging these two modules, THTN crafts an improved node representation,
capturing both local and global topological expressions. Extensive experiments
conducted on node classification tasks demonstrate that the performance of the
proposed model consistently exceeds that of the existing approaches.
