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：超图因其能够刻画高阶关系，已成为传统图的重要扩展。尽管图神经网络（GNNs）在图表示学习方面表现出色，但其向超图的扩展因其复杂结构而面临挑战。此外，当前作为GNN特殊变体的超图Transformer主要利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，本文提出了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，以学习图中的高阶关系。随后，我们设计了一个简洁而有效的结构与空间编码模块，将节点的拓扑信息与空间信息融入其表征中。进一步，我们提出了一种结构感知的自注意力机制，能够从语义和结构双重视角发现重要的节点与超边。通过协同利用这两个模块，THTN构建出增强的节点表征，同时捕获局部与全局的拓扑表达。在节点分类任务上进行的大量实验表明，所提模型的性能持续超越现有方法。