  We propose an extension to the transformer neural network architecture for
general-purpose graph learning by adding a dedicated pathway for pairwise
structural information, called edge channels. The resultant framework - which
we call Edge-augmented Graph Transformer (EGT) - can directly accept, process
and output structural information of arbitrary form, which is important for
effective learning on graph-structured data. Our model exclusively uses global
self-attention as an aggregation mechanism rather than static localized
convolutional aggregation. This allows for unconstrained long-range dynamic
interactions between nodes. Moreover, the edge channels allow the structural
information to evolve from layer to layer, and prediction tasks on edges/links
can be performed directly from the output embeddings of these channels. We
verify the performance of EGT in a wide range of graph-learning experiments on
benchmark datasets, in which it outperforms Convolutional/Message-Passing Graph
Neural Networks. EGT sets a new state-of-the-art for the quantum-chemical
regression task on the OGB-LSC PCQM4Mv2 dataset containing 3.8 million
molecular graphs. Our findings indicate that global self-attention based
aggregation can serve as a flexible, adaptive and effective replacement of
graph convolution for general-purpose graph learning. Therefore, convolutional
local neighborhood aggregation is not an essential inductive bias.
