English(EN) AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification

新论文探讨 GNN 的通用用途和可靠决策

作者 PulseAugur 编辑部 · [3 个来源] · 2026-06-01 04:00

两篇新研究论文探讨了图神经网络 (GNN) 的进展。第一篇论文为机器学习工程师提供了 GNN 的入门概述，详细介绍了其框架、应用以及过平滑等挑战。第二篇论文介绍了 AbstainGNN，这是一个新颖的框架，旨在使 GNN 在不确定性高时能够弃权预测，从而提高在安全关键应用中的可靠性。 AI

影响提高 GNN 在关键应用中的可靠性，并为更广泛的应用提供基础知识。

排序理由两篇在 arXiv 上发表的学术论文，详细介绍了图神经网络的新研究。

AI 生成摘要 · Google Gemini · 来自 3 个来源。我们如何撰写摘要 →

报道来源 [3]

arXiv cs.LG TIER_1 English(EN) · Megha Khosla · 2026-06-03 04:00

图结构对图神经网络成员推理风险的影响

arXiv:2601.17130v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely used for tasks such as node classification and link prediction, but their use in sensitive settings raises concerns about training-data leakage. Prior work on privacy leakage in GNNs large…
arXiv cs.AI TIER_1 English(EN) · James H. Tanis, Chris Giannella, Adrian V. Mariano, Daoud Meerzaman · 2026-06-02 04:00

面向机器学习工程师的图神经网络导论

arXiv:2412.19419v2 Announce Type: replace-cross Abstract: Graph neural networks are deep neural networks designed for graphs with attributes attached to nodes or edges. The number of research papers in the literature concerning these models is growing rapidly due to their impress…
arXiv cs.LG TIER_1 English(EN) · Xixun Lin, Zhiheng Zhou, Zhengyin Zhang, Yancheng Chen, Shuai Zhang, Ge Zhang, Shichao Zhu, Lixin Zou, Chuan Zhou, Peng Zhang, Shirui Pan, Yanan Cao · 2026-06-01 04:00

AbstainGNN：教会图神经网络在图分类任务中弃权

arXiv:2605.30786v1 Announce Type: new Abstract: Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performance improvements for graph classification. However, ex…