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English(EN) FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks

FLASH机制增强时序图神经网络性能

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FLASH，一种旨在提高时序图神经网络（TGNNs）性能的新型机制。FLASH是一种可学习且图自适应的邻域选择方法，解决了TGNNs中当前使用的静态采样启发式方法的局限性。通过无缝集成并使用自监督排序损失进行端到端训练，FLASH在各种基准测试中均显示出持续且显著的性能提升，为未来链接预测提供了从历史交互中聚合时序信号的更有效方法。 AI

影响 这项研究为动态图中历史数据处理提供了一种更具适应性和效率的方法，有望提高TGNNs中的链接预测准确性。

排序理由 该集群包含一篇详细介绍时序图神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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FLASH机制增强时序图神经网络性能

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该集群包含一篇详细介绍时序图神经网络新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Or Feldman, Krishna Sri Ipsit Mantri, Carola-Bibiane Sch\"onlieb, Chaim Baskin, Moshe Eliasof ·

    快讯：时间图神经网络中历史自适应采样灵活学习

    arXiv:2504.07337v2 Announce Type: replace Abstract: Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Hence, temporal graph neural networks (TGNNs) often …

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