SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning

Abstract

Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability. Most previous SGRL models face computational issues related to the high cost of extracting subgraphs for each training or testing query. Recently, SUREL was proposed to accelerate SGRL, which samples random walks offline and joins these walks online as a proxy of subgraphs for prediction. Thanks to the reusability of sampled walks across different queries, SUREL achieves state-of-the-art performance in terms of scalability and prediction accuracy. However, SUREL still suffers from high computational overhead caused by node redundancy in sampled walks. In this work, we propose a novel framework SUREL+ that upgrades SUREL by using node sets instead of walks to represent subgraphs. By definition, such set-based representations avoid repeated nodes, but node sets can be irregular in size. To solve this issue, we design a dedicated sparse data structure to efficiently store and access node sets, and provide a specialized operator to join them in parallel batches. SUREL+ is modularized to support multiple types of set samplers, structural features, and neural encoders to complement the loss of structural information after the reduction from walks to sets. Extensive experiments have been performed to verify the effectiveness of SUREL+ in the prediction tasks of links, relation types, and higher-order patterns. SUREL+ achieves 3--11× speedups of SUREL while maintaining comparable or even better prediction performance; compared to other SGRL baselines, SUREL+ achieves ~20× speedups and significantly improves the prediction accuracy.