mlsanityData to 14 Oct 2026

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

arXiv 2507.14570 ↗First posted 19 Jul 2025cs.LG cs.AIcited by 2

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Abstract (arXiv)

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 GPU memory, particularly when dealing with the neighbor explosion issue inherent in large-scale graphs. This paper introduces a scalable, low-cost, flexible, and efficient GNN framework called LPS-GNN, which can perform representation learning on 100 billion graphs 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 LPMetis. In particular, LPMetis outperforms current state-of-the-art (SOTA) approaches on various evaluation metrics. In addition, our paper 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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 65 references that are in the map. First posted on arXiv 19 Jul 2025.

Builds on

33 of 65 references.

References · 33

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21 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Networkcited by 0
  2. 2026FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanismcited by 0