LPS-GNN: Deploying Graph Neural Networks on Graphs with 100-Billion Edges

Abstract

Graph Neural Networks (GNNs) have emerged as powerful tools for various graph mining tasks, yet existing scalable solutions often struggle to balance execution efficiency with prediction accuracy. These difficulties stem from iterative message-passing techniques, which place significant computational demands and require extensive Graphics Processing Unit (GPU) memory, particularly when dealing with the neighbor explosion issue inherent in large-scale graphs. This article introduces a scalable, low-cost, flexible, and efficient GNN framework called LPS-GNN, which can perform representation learning on 100billion-edge graph with a single GPU in 10 hours and shows a 13.8% improvement in User Acquisition scenarios. We examine existing graph partitioning methods and design a superior graph partition algorithm named Label Propagation with METIS (LPMetis). In particular, LPMetis outperforms current state-of-the-art (SOTA) approaches on various evaluation metrics. In addition, our article proposes a subgraph augmentation strategy to enhance the model’s predictive performance. It exhibits excellent compatibility, allowing the entire framework to accommodate various GNN algorithms. Successfully deployed on the Tencent platform, LPS-GNN has been tested on public and real-world datasets, achieving performance lifts of 8.24% to 13.89% over SOTA models in online applications.