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.

翻译：超图凭借其刻画高阶关系的能力，已成为传统图的重要拓展。尽管图神经网络（GNN）在图表示学习领域表现出色，但由于超图的复杂结构，其向超图的推广面临挑战。此外，当前作为GNN特殊变体的超图Transformer，采用基于语义特征的自注意力机制，忽略了节点与超边的拓扑属性。为应对这些挑战，我们提出一种拓扑引导的超图Transformer网络（THTN）。在该模型中，我们首先从图中构建超图并保留其结构本质，以学习图内的高阶关系；继而设计一个简洁而有效的结构与空间编码模块，将节点的拓扑与空间信息融入其表示；进一步提出一种结构感知的自注意力机制，从语义与结构双重角度发现重要节点与超边。通过利用这两个模块，THTN构建了改进的节点表示，同时捕获局部与全局拓扑表达。在节点分类任务上的大量实验表明，所提模型的性能始终优于现有方法。