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Data"],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>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\u2009hours 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\u2019s predictive performance. It exhibits excellent compatibility, allowing the entire framework to accommodate various GNN algorithms. 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