Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation
This work addresses a domain-specific problem in hypergraph representation learning for researchers in machine learning and network analysis, offering an incremental improvement by integrating structural insights into existing transformer frameworks.
The paper tackles the challenge of extending graph neural networks to hypergraphs by proposing a Topology-guided Hypergraph Transformer Network (THTN) that incorporates topological and spatial information, resulting in improved performance on node classification tasks compared to existing methods.
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.