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

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 associated with the high cost of extracting subgraphs for each training or testing query. Recently, SUREL has been proposed as a new framework to accelerate SGRL, which samples random walks offline and joins these walks as subgraphs online for prediction. Due to the reusability of sampled walks across different queries, SUREL achieves state-of-the-art performance in both 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. This set-based representation avoids node duplication by definition, but the sizes of node sets can be irregular. To address this issue, we design a dedicated sparse data structure to efficiently store and fast index 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 due to the reduction from walks to sets. Extensive experiments have been performed to validate SUREL+ in the prediction tasks of links, relation types, and higher-order patterns. SUREL+ achieves 3-11$\times$ speedups of SUREL while maintaining comparable or even better prediction performance; compared to other SGRL baselines, SUREL+ achieves $\sim$20$\times$ speedups and significantly improves the prediction accuracy.