  Albeit having gained significant progress lately, large-scale graph
representation learning remains expensive to train and deploy for two main
reasons: (i) the repetitive computation of multi-hop message passing and
non-linearity in graph neural networks (GNNs); (ii) the computational cost of
complex pairwise contrastive learning loss. Two main contributions are made in
this paper targeting this twofold challenge: we first propose an adaptive-view
graph neural encoder (AVGE) with a limited number of message passing to
accelerate the forward pass computation, and then we propose a structure-aware
group discrimination (SAGD) loss in our framework which avoids inefficient
pairwise loss computing in most common GCL and improves the performance of the
simple group discrimination. By the framework proposed, we manage to bring down
the training and inference cost on various large-scale datasets by a
significant margin (250x faster inference time) without loss of the
downstream-task performance.
