Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs). However, GNNs handling heterogeneous graphs (HeGs), which contain several entity and relation types, all have shortcomings in handling attention. Most GNNs that learn graph attention for HeGs learn either node-level or relation-level attention, but not both, limiting their ability to predict both important entities and relations in the HeG. Even the best existing method that learns both levels of attention has the limitation of assuming graph relations are independent and that its learned attention disregards this dependency association. To effectively model both multi-relational and multi-entity large-scale HeGs, we present Bi-Level Attention Graph Neural Networks (BA-GNN), scalable neural networks (NNs) that use a novel bi-level graph attention mechanism. BA-GNN models both node-node and relation-relation interactions in a personalized way, by hierarchically attending to both types of information from local neighborhood contexts instead of the global graph context. Rigorous experiments on seven real-world HeGs show BA-GNN consistently outperforms all baselines, and demonstrate quality and transferability of its learned relation-level attention to improve performance of other GNNs.

翻译：摘要：近期带有注意力机制的图神经网络（GNNs）在历史上局限于小规模同构图（HoGs）。然而，处理包含多种实体类型与关系类型的异构图（HeGs）的GNNs在注意力机制方面均存在缺陷。多数学习异构图注意力的GNNs仅能学习节点级或关系级注意力之一，而非两者兼备，这限制了其预测异构图中的关键实体与关系的能力。即便现有最佳方法能学习双层级注意力，仍存在假设图关系彼此独立、且所学习的注意力忽略这种依赖关联的问题。为有效建模兼具多关系与多实体的大规模异构图，我们提出双层级注意力图神经网络（BA-GNN）——一种可扩展的神经网络，采用新颖的双层级图注意力机制。BA-GNN通过分层关注局部邻域上下文而非全局图上下文中的两类信息，以个性化方式建模节点-节点与关系-关系交互。在七个真实异构图上开展的严格实验表明，BA-GNN持续优于所有基线模型，其学习的关系级注意力具备高质量与可迁移性，可提升其他GNNs的性能。