Hierarchical Attention Models for Multi-Relational Graphs

arXiv:2404.09365v1 Announce Type: new
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 …

abstract architectures arxiv attention cs.lg data embeddings graph graphs hierarchical learn network networks neural network node relational through type