English(EN) Online Bayesian Node Classification on Inductive Graphs under Distribution Shift

新的VBLL方法增强了演化图上的在线节点分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种名为变分贝叶斯最后一层（VBLL）的新方法，用于演化图上的在线节点分类。该方法解决了归纳泛化和校准不确定性的挑战，这对于动态图环境和安全敏感应用至关重要。VBLL联合训练图神经网络编码器和近似的最后一层后验，并在测试时使用在线拉普拉斯更新进行流式后验更新。在五个基准测试上的实验表明，VBLL在准确性和负对数似然方面表现优越，在Cora和ogbn-arxiv等数据集上显著优于其他方法。 AI

影响提高了动态图分析的准确性和不确定性校准，这对于具有演化数据的实际应用至关重要。

排序理由学术论文，详细介绍了一种新的图节点分类方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jinwen Xu, Gonzalo Mateos Buckstein, Qin Lu · 2026-09-15 04:00

分布偏移下归纳图上的在线贝叶斯节点分类

arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and calibrated uncertainty for safety-sensitive applications. Standard graph neural ne…

本文如何被排名

报道来源 [1]

分布偏移下归纳图上的在线贝叶斯节点分类

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