  In this paper, we present Conjoint Attentions (CAs), a class of novel
learning-to-attend strategies for graph neural networks (GNNs). Besides
considering the layer-wise node features propagated within the GNN, CAs can
additionally incorporate various structural interventions, such as node cluster
embedding, and higher-order structural correlations that can be learned outside
of GNN, when computing attention scores. The node features that are regarded as
significant by the conjoint criteria are therefore more likely to be propagated
in the GNN. Given the novel Conjoint Attention strategies, we then propose
Graph conjoint attention networks (CATs) that can learn representations
embedded with significant latent features deemed by the Conjoint Attentions.
Besides, we theoretically validate the discriminative capacity of CATs. CATs
utilizing the proposed Conjoint Attention strategies have been extensively
tested in well-established benchmarking datasets and comprehensively compared
with state-of-the-art baselines. The obtained notable performance demonstrates
the effectiveness of the proposed Conjoint Attentions.
