English(EN) AnchorRM: Parallel Intra-Batch State Reconstruction with Anchor-Based Residual Memory for Temporal Graph Neural Networks

AnchorRM 提高了 TGN 的训练速度和可扩展性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了 AnchorRM，这是一种新颖的内存机制，旨在提高时序图神经网络 (TGN) 的效率和可扩展性。这种新方法通过使用固定的锚点内存并从同一批次中先前交互推断状态演变，并行重构批内节点状态。该方法避免了通常限制 TGN 训练速度和批次扩展性的顺序递归更新。实验表明，AnchorRM 在保持具有竞争力的性能的同时，可以实现显著的训练加速和改进的批次可扩展性。 AI

影响该方法可以显著加速时序图学习模型的训练并提高其可扩展性。

排序理由该项目是一篇研究论文，详细介绍了一种用于时序图神经网络的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Haroon Gharwi, Kai Shu, Yue Dai · 2026-10-21 04:00

AnchorRM：基于锚点的残差内存，用于图神经网络的并行批内状态重构

arXiv:2610.27558v1 Announce Type: cross Abstract: Memory-based temporal graph neural networks (TGN) rely on recurrent node memories to capture evolving interaction histories. However, temporal mini-batching introduces a trade-off between computational efficiency and temporal fide…

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AnchorRM：基于锚点的残差内存，用于图神经网络的并行批内状态重构

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