  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.
