Training Large-Scale Graph Neural Networks via Graph Partial Pooling

Graph Neural Networks (GNNs) are powerful tools for graph representation learning, but they face challenges when applied to large-scale graphs due to substantial computational costs and memory requirements. To address scalability limitations, various methods have been proposed, including sampling-based and decoupling-based methods. However, these methods have their limitations: sampling-based methods inevitably discard some link information during the sampling process, while decoupling-based methods require alterations to the model's structure, reducing their adaptability to various GNNs. This paper proposes a novel graph pooling method, Graph Partial Pooling (GPPool), for scaling GNNs to large-scale graphs. GPPool is a versatile and straightforward technique that enhances training efficiency while simultaneously reducing memory requirements. GPPool constructs small-scale pooled graphs by pooling partial nodes into supernodes. Each pooled graph consists of supernodes and unpooled nodes, preserving valuable local and global information. Training GNNs on these graphs reduces memory demands and enhances their performance. Additionally, this paper provides a theoretical analysis of training GNNs using GPPool-constructed graphs from a graph diffusion perspective. It shows that a GNN can be transformed from a large-scale graph into pooled graphs with minimal approximation error. A series of experiments on datasets of varying scales demonstrates the effectiveness of GPPool.