Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation

• URL: http://arxiv.org/abs/2412.12158v1
• Date: Wed, 11 Dec 2024 12:03:33 GMT
• Title: Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
• Authors: Mengfan Li, Xuanhua Shi, Chenqi Qiao, Teng Zhang, Hai Jin,
• Abstract summary: We propose the Hyperbolic Hypergraph Neural Network (H2GNN), whose essential component is the hyper-star message passing. We compare H2GNN with 15 baselines on knowledge hypergraphs, and it outperforms state-of-the-art approaches in both node classification and link prediction tasks.
• Score: 19.14148664582895
• License:
• Abstract: Knowledge hypergraphs generalize knowledge graphs using hyperedges to connect multiple entities and depict complicated relations. Existing methods either transform hyperedges into an easier-to-handle set of binary relations or view hyperedges as isolated and ignore their adjacencies. Both approaches have information loss and may potentially lead to the creation of sub-optimal models. To fix these issues, we propose the Hyperbolic Hypergraph Neural Network (H2GNN), whose essential component is the hyper-star message passing, a novel scheme motivated by a lossless expansion of hyperedges into hierarchies. It implements a direct embedding that consciously incorporates adjacent entities, hyper-relations, and entity position-aware information. As the name suggests, H2GNN operates in the hyperbolic space, which is more adept at capturing the tree-like hierarchy. We compare H2GNN with 15 baselines on knowledge hypergraphs, and it outperforms state-of-the-art approaches in both node classification and link prediction tasks.

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