  Lots of neural network architectures have been proposed to deal with learning
tasks on graph-structured data. However, most of these models concentrate on
only node features during the learning process. The edge features, which
usually play a similarly important role as the nodes, are often ignored or
simplified by these models. In this paper, we present edge-featured graph
attention networks, namely EGATs, to extend the use of graph neural networks to
those tasks learning on graphs with both node and edge features. These models
can be regarded as extensions of graph attention networks (GATs). By reforming
the model structure and the learning process, the new models can accept node
and edge features as inputs, incorporate the edge information into feature
representations, and iterate both node and edge features in a parallel but
mutual way. The results demonstrate that our work is highly competitive against
other node classification approaches, and can be well applied in edge-featured
graph learning tasks.
