  Graph attention networks (GATs) have been recognized as powerful tools for
learning in graph structured data. However, how to enable the attention
mechanisms in GATs to smoothly consider both structural and feature information
is still very challenging. In this paper, we propose Graph Joint Attention
Networks (JATs) to address the aforementioned challenge. Different from
previous attention-based graph neural networks (GNNs), JATs adopt novel joint
attention mechanisms which can automatically determine the relative
significance between node features and structural coefficients learned from
graph topology, when computing the attention scores. Therefore, representations
concerning more structural properties can be inferred by JATs. Besides, we
theoretically analyze the expressive power of JATs and further propose an
improved strategy for the joint attention mechanisms that enables JATs to reach
the upper bound of expressive power which every message-passing GNN can
ultimately achieve, i.e., 1-WL test. JATs can thereby be seen as most powerful
message-passing GNNs. The proposed neural architecture has been extensively
tested on widely used benchmarking datasets, and has been compared with
state-of-the-art GNNs for various downstream predictive tasks. Experimental
results show that JATs achieve state-of-the-art performance on all the testing
datasets.
