Hierarchical Attention Models for Multi-Relational Graphs

• URL: http://arxiv.org/abs/2404.09365v1
• Date: Sun, 14 Apr 2024 21:37:39 GMT
• Title: Hierarchical Attention Models for Multi-Relational Graphs
• Authors: Roshni G. Iyer, Wei Wang, Yizhou Sun,
• Abstract summary: We present Bi-Level Attention-Based Graph Convolutional Networks (BR-GCN) BR-GCN models use bi-level attention to learn node embeddings through (1) node-level attention, and (2) relation-level attention. We show that BR-GCN's attention mechanism is both scalable and more effective in learning compared to state-of-the-art GNNs.
• Score: 40.143721982295915
• License: http://creativecommons.org/licenses/by/4.0/
• 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. The 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. The 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. The BR-GCN 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% as a stand-alone model, and on link prediction, BR-GCN outperforms baselines from 0.02% to 7.40% as an auto-encoder model. 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.

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