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English(EN) Monte Carlo Random Walk Kernel

新的蒙特卡洛随机游走核提供更快的图分析

一种名为蒙特卡洛随机游走核(MCRWK)的新核估计方法已被开发出来,它提供了传统随机游走核(RWK)的替代方案。MCRWK 的每对成本与图的大小无关,这是通过归一化核系数并通过随机游走估计贡献来实现的。这种方法允许在不构建乘积图的情况下近似 RWK,其运行时间和准确性由蒙特卡洛样本预算控制。实验表明,在大型合成和真实图数据集上,MCRWK 比现有方法更快、更准确。 AI

影响 引入了一种更有效的图分析方法,可能影响涉及图数据的机器学习任务。

排序理由 该集群包含一篇详细介绍图分析新核估计方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的蒙特卡洛随机游走核提供更快的图分析

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该集群包含一篇详细介绍图分析新核估计方法的研究论文。[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) · Arslan Iamaletdinov, Elfat Sabitov, Konstantin Sorokin ·

    Monte Carlo Random Walk Kernel

    arXiv:2610.40956v1 Announce Type: new Abstract: We introduce the Monte Carlo Random Walk Kernel (MCRWK), an estimator whose per-pair cost is independent of graph size and derived directly from the random walk kernel (RWK) expansion. By normalizing the kernel coefficients, MCRWK s…