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arXiv AI By Siqi Gu, Gaozhen Ma, Liheng Huang, Weiqi He, Jiawen Luo, Jiaming Wu, Lixin Zheng

Decoupled Prune-and-Reconstruct: Training on a Compact Core with Double-Layer Sparsification

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Decoupled Prune-and-Reconstruct (DPR) introduces a double‑layer sparsification strategy for graph neural networks that first prunes nodes to reduce memory usage and then reconstructs the full graph during inference. By training on a compact core of 30% of the nodes, DPR achieves 96.9% of the accuracy of a dense model while using only 1,450 MiB of peak memory on a single 6 GB GPU, outperforming edge‑sparsification baselines and reducing training memory by 50.1%. The method scales to millions of nodes, enabling all‑node inference on VRAM‑limited hardware without incurring a permanent accuracy loss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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