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English(EN) Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

图神经网络提高了节点中心性近似的速度和可转移性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了图神经网络（GNN）来近似计算节点中心性指标，如介数中心性和紧密度中心性，这些指标精确计算成本很高。该研究表明，GNN可以学习跨不同图拓扑的可转移结构表示，从而提高可扩展性和速度。虽然模型在近似介数中心性方面显示出显著的加速潜力，但紧密度中心性仍然对图结构敏感，这仍然是一个挑战。 AI

影响 这项研究可以实现对社会科学和生物学等领域的大型复杂网络的更快分析。

排序理由 该集群包含一篇详细介绍使用GNN近似图数量的新方法的论文。

在 arXiv cs.LG 阅读 →

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图神经网络提高了节点中心性近似的速度和可转移性

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该集群包含一篇详细介绍使用GNN近似图数量的新方法的论文。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Samra Sana, Giorgio Mantica, Saul Imbrici ·

    用于可扩展和可迁移节点中心性近似的图神经网络

    arXiv:2607.09372v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centralit…

  2. arXiv cs.LG TIER_1 English(EN) · Saul Imbrici ·

    用于可扩展和可迁移节点中心性近似的图神经网络

    Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly. This paper investigates GNNs for scalable approximation of betweenness and closeness centrality, formulated as a node-ranking problem. Exact c…

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