  Subgraph-based graph representation learning (SGRL) has been recently
proposed to deal with some fundamental challenges encountered by canonical
graph neural networks (GNNs), and has demonstrated advantages in many important
data science applications such as link, relation and motif prediction. However,
current SGRL approaches suffer from scalability issues since they require
extracting subgraphs for each training or test query. Recent solutions that
scale up canonical GNNs may not apply to SGRL. Here, we propose a novel
framework SUREL for scalable SGRL by co-designing the learning algorithm and
its system support. SUREL adopts walk-based decomposition of subgraphs and
reuses the walks to form subgraphs, which substantially reduces the redundancy
of subgraph extraction and supports parallel computation. Experiments over six
homogeneous, heterogeneous and higher-order graphs with millions of nodes and
edges demonstrate the effectiveness and scalability of SUREL. In particular,
compared to SGRL baselines, SUREL achieves 10$\times$ speed-up with comparable
or even better prediction performance; while compared to canonical GNNs, SUREL
achieves 50% prediction accuracy improvement.
