提出H2GNN模型，用双曲空间建模多关系知识超图，更精准捕捉层级结构。
Hyperbolic Hypergraph Neural Networks for Multi-Relational Knowledge Hypergraph Representation
- 设计超星消息传递机制，无损展开超边并保留邻接信息
- 在节点分类和链接预测任务上超越15个基线模型
- 适合处理具有树状层次结构的复杂多关系数据
知识超图通过超边连接多个实体以描述复杂关系，现有方法或将其转换为二元关系，或视超边为孤立单元，均导致信息损失且可能生成次优模型。为此，我们提出双曲超图神经网络（H2GNN），其核心是受超边无损展开启发的超星消息传递机制，直接嵌入相邻实体、超关系及实体位置感知信息。如其名所示，H2GNN在双曲空间中运行，更擅长捕捉树状层次结构。我们在知识超图上与15个基线方法对比，结果表明，该模型在节点分类和链接预测任务中均优于当前最先进方法。
原文摘要 · Abstract (English)
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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