Empowering Graph Representation Learning with Paired Training and Graph Co-Attention
Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.The setup of prediction tasks over entire graphs, however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs.Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them.We first show that such a setup provides natural benefits on a pairwise graph classification task, and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark.Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.
We use graph co-attention in a paired graph training system for graph classification and regression.This paper injects a multi-head co-attention mechanism in GCN that allows one drug to attend to another drug during drug side effect prediction.A method to extend graph-based learning with a co-attentional layer, which outperforms other previous ones on a pairwise graph classification task.