Context-Aware Graph Attention Networks
This work addresses a limitation in GNNs for graph data representation, offering a domain-specific improvement for semi-supervised learning applications.
The paper tackles the problem of existing Graph Neural Networks (GNNs) ignoring edge representation learning by proposing CaGAT, a unified model that learns context-aware attention for edges and nodes simultaneously, resulting in promising experimental results on benchmark datasets for semi-supervised learning tasks.
Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation only which usually ignores the learning of edge (weight) representation. In this paper, we propose a novel unified GNN model, named Context-aware Adaptive Graph Attention Network (CaGAT). CaGAT aims to learn a context-aware attention representation for each graph edge by further exploiting the context relationships among different edges. In particular, CaGAT conducts context-aware learning on both node feature representation and edge (weight) representation simultaneously and cooperatively in a unified manner which can boost their respective performance in network training. We apply CaGAT on semi-supervised learning tasks. Promising experimental results on several benchmark datasets demonstrate the effectiveness and benefits of CaGAT.