D-HAT: Dynamic Hypergraph Representation Learning with Attention-Based Multi-Level Hypergraph Sampling

초록

Hypergraph Neural Networks (HNNs) leverage higher-order interactions in graph-structured data to enable effective representation learning across a wide range of applications. Due to the issue of incorporating irrelevant relations in full hypergraphs, it is crucial to adopt a hypergraph sampling method that efficiently captures substructures while preserving representational quality. However, existing hypergraph sampling methods that target only nodes or hyperedges suffer from subgraph disconnection issues and neglect of node importance due to the randomness in sampling and use of static computational sub-hypergraphs. In this paper, we propose D-HAT, a hypergraph learning framework that dynamically constructs representative sub-hypergraphs through a novel attention-based multi-level hypergraph sampling strategy during the training of HNNs. To prioritize informative neighbors and enhance the representational quality of sub-hypergraphs during training, we develop a new attention-based HNNs incorporating attention-guided aggregation and dense skip connections. To the best of our knowledge, this paper is the first to quantitatively compare various hypergraph sampling methods for hypergraph representation learning. Experiments on real-world graph datasets demonstrate the effectiveness of D-HAT, which consistently achieves higher accuracy compared to existing hypergraph sampling methods. © 2025 Copyright held by the owner/author(s).

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