新算法使稀疏图上的图核计算速度提高27倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-27 04:00

研究人员开发了新的算法，可以在线性时间内计算稀疏图上的通用随机游走图核，这比之前的三次时间方法有了显著改进。这些算法通过近似图嵌入而无需将整个图存储在内存中，从而实现了高效的图核学习，并能够扩展到海量数据集。新方法速度提高了27倍，并且可以处理比以前可行的大128倍的图。 AI

影响能够更有效地处理大规模图数据，有可能加速依赖于图嵌入和核方法的领域的研究。

排序理由这是一篇详细介绍图核计算新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv stat.ML TIER_1 English(EN) · Krzysztof Choromanski, Isaac Reid, Arijit Sehanobish, Avinava Dubey · 2026-08-27 04:00

稀疏图上计算通用随机游走图核的最优时间复杂度算法

arXiv:2410.10368v3 Announce Type: replace-cross Abstract: We present the first linear time complexity randomized algorithms for unbiased approximation of the celebrated family of general random walk kernels (RWKs) for sparse graphs. This includes both labelled and unlabelled inst…