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English(EN) Nonlinear Laplacians Improve Signed-Directed Graph Learning

新的非线性拉普拉斯算子增强了用于有向符号数据的图神经网络

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新的非线性拉普拉斯算子，称为 NLSD，专门用于有向符号图。该算子通过计算特定于节点的势能，并仅在势能差异与边的方向一致的边上利用消息传递技术，来扩展现有的有向和符号图概念。基于该算子的 NLSD-GNN 框架在各种数据集的节点分类和链接预测任务中表现出卓越的性能，有效地整合了符号和方向信息。 AI

影响 引入了一种新颖的算子，可以提高图学习任务（如节点分类和链接预测）的性能。

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

在 arXiv cs.LG 阅读 →

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新的非线性拉普拉斯算子增强了用于有向符号数据的图神经网络

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该集群包含一篇详细介绍图学习新方法的论文。[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) · Ali Parviz, Yuichi Yoshida ·

    非线性拉普拉斯算子改进有向符号图学习

    arXiv:2608.00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We introduce a non…

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