(#87 / Sess. 2) Bi-Level Attention Neural Architectures for Relational Data
Abstract
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relational graph convolutions, to effectively operate on highly multi-relational data. BR-GCN models use bi-level attention to learn node embeddings through (1) node-level attention, and (2) relation-level attention. BR-GCN’s node-level self-attentional layers use intra-relational graph interactions to learn relation-specific node embeddings using a weighted aggregation of neighborhood features in a sparse subgraph region. BR-GCN's relation-level self-attentional layers use inter-relational graph interactions to learn the final node embeddings using a weighted aggregation of relation-specific node embeddings. BR-GCN's bi-level attention mechanism extends Transformer-based multiplicative attention from the natural language processing (NLP) domain, and Graph Attention Networks (GAT)-based attention, to large-scale heterogeneous graphs (HGs). On node classification, BR-GCN outperforms baselines from 0.29% to 14.95%, and on the link prediction task, outperforms baselines from 0.02% to 7.40% and suggests to enrich HG embedding models. We also conduct ablation studies to evaluate the quality of BR-GCN's relation-level attention and discuss how its learning of graph structure may be transferred to enrich other Graph Neural Networks (GNNs). Through various experiments, we show that BR-GCN's attention mechanism is both scalable and more effective in learning compared to state-of-the-art GNNs.