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English(EN) STAMP: Symmetric Temporal Averaging Message Passing for Domain Adaptation in Chronologically Split Graphs

新的STAMP方法改进了时间分割图数据的域自适应

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的消息传递算子STAMP，专为时间分割图的域自适应而设计。该方法解决了当在历史图数据上训练的模型应用于近期数据时，由于时间分布变化而导致的性能下降问题。STAMP通过平衡不同时间戳的消息，确保一阶矩不变性并保持可扩展性。该方法在OGBN-Arxiv和OGBN-MAG等基准测试中取得了最先进的成果，并在时间分割的Pokec数据集上持续提高了性能。 AI

影响 这项研究为处理时变数据分布的图基AI模型的鲁棒性提供了一种新技术。

排序理由 该集群包含一篇详细介绍图学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的STAMP方法改进了时间分割图数据的域自适应

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该集群包含一篇详细介绍图学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Joo Young Park, Sejun Park, Hyunwoo Park ·

    STAMP：用于时间分割图领域自适应的对称时间平均消息传递

    arXiv:2411.10957v2 Announce Type: replace-cross Abstract: This paper addresses domain adaptation in graph-structured data under chronological splits. We consider transductive semi-supervised node classification (SSNC), in which labels of historical nodes are used to predict label…

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