  Link prediction on graphs is a fundamental problem. Subgraph representation
learning approaches (SGRLs), by transforming link prediction to graph
classification on the subgraphs around the links, have achieved
state-of-the-art performance in link prediction. However, SGRLs are
computationally expensive, and not scalable to large-scale graphs due to
expensive subgraph-level operations. To unlock the scalability of SGRLs, we
propose a new class of SGRLs, that we call Scalable Simplified SGRL (S3GRL).
Aimed at faster training and inference, S3GRL simplifies the message passing
and aggregation operations in each link's subgraph. S3GRL, as a scalability
framework, accommodates various subgraph sampling strategies and diffusion
operators to emulate computationally-expensive SGRLs. We propose multiple
instances of S3GRL and empirically study them on small to large-scale graphs.
Our extensive experiments demonstrate that the proposed S3GRL models scale up
SGRLs without significant performance compromise (even with considerable gains
in some cases), while offering substantially lower computational footprints
(e.g., multi-fold inference and training speedup).
