  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.
