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English(EN) Node-level Graph Neural Architecture Search Framework

新的N-GNAS框架通过为节点特征定制架构来增强GNN

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新颖的节点级图神经网络架构搜索（N-GNAS）框架，旨在增强图神经网络（GNNs）的性能。与将统一操作应用于所有节点的传统方法不同，N-GNAS根据不同节点子集的结构和特征特性动态选择合适的网络架构。这种方法旨在缓解过平滑等问题，并提高非欧几里得数据处理的准确性。在八个数据集上的实验表明，N-GNAS在CiteSeer数据集上取得了78.26%的准确率，优于现有的GNAS技术和人工设计的GNN。 AI

影响 这一新框架有望为处理复杂、非结构化数据的任务带来更高效、更准确的图神经网络模型。

排序理由 该条目是一篇学术论文，详细介绍了一种新算法及其实验结果。[lever_c_demoted from research: ic=1 ai=1.0]

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新的N-GNAS框架通过为节点特征定制架构来增强GNN

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Lintao Yanga, Sirui Lia, Yaqing Wang, Pietro Li\`o, Xu Shen, Baisong Liu, Chengbin Peng ·

    节点级图神经网络架构搜索框架

    arXiv:2610.09297v1 Announce Type: new Abstract: In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data. Nevertheless…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    节点级图神经网络架构搜索框架

    In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data. Nevertheless, traditional approaches typically apply uniform…

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