CLhypformer: Topology-Aware Hyperbolic Hypergraph Transformer for Graph Representation Learning

Learning effective representations from complex graphs requires simultaneously capturing hierarchical geometry and higher-order topology. However, conventional Euclidean embeddings can distort hierarchical relationships, while existing hypergraph construction strategies based on Euclidean proximity or random walks may introduce structurally inconsistent connections. We propose CLhypformer, a topology-aware hyperbolic hypergraph Transformer that jointly addresses these geometric and topological limitations. CLhypformer constructs hyperedges through hyperbolic K-nearest-neighbor relationships, enabling topology construction in a representation space that better accommodates hierarchical structures. It further employs a topology-aware discriminator to generate asymmetric structural views and a debiased three-level contrastive objective to align node, hyperedge, and cross-level representations. Higher-order information is then propagated through a hyperbolic hypergraph Transformer for downstream node classification. Experiments on multiple graph benchmarks show that CLhypformer achieves competitive or superior classification performance and remains robust under severe structural masking. Ablation and qualitative analyses further demonstrate that hyperbolic topology construction and topology-aware contrastive alignment both contribute to the effectiveness of the proposed framework.