研究人员开发了一种新的图匹配算法，该算法的运行时间接近二次方，并在特定条件下实现了近乎精确的恢复。该算法利用局部树相关性检验和基于秩的方法，避免了计算密集型阈值计算的需要。这项工作为发散度量下的树相关性检验建立了新的分析，并展示了图匹配的阈值，最终将基于秩和基于阈值的方法耦合以改进恢复。 AI
影响 这项研究为图论和算法的基础做出了贡献，可能影响依赖于图分析和匹配的AI应用。
排序理由 详细介绍新算法和理论结果的学术论文。
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arXiv:2607.09087v1 Announce Type: cross Abstract: This paper studies graph matching under the correlated $\text{Erd\H{o}s-R\'{e}nyi}$ (ER) graph pair model. This model first samples an $\mathrm{ER}(n,\frac{\lambda}{ns})$ base graph, whose edges are then independently subsampled t…
This paper studies graph matching under the correlated $\text{Erdős-Rényi}$ (ER) graph pair model. This model first samples an $\mathrm{ER}(n,\fracλ{ns})$ base graph, whose edges are then independently subsampled twice with probability $s$ to produce two correlated $\mathrm{ER}(n…
This paper studies graph matching under the correlated $\text{Erdős-Rényi}$ (ER) graph pair model. This model first samples an $\mathrm{ER}(n,\fracλ{ns})$ base graph, whose edges are then independently subsampled twice with probability $s$ to produce two correlated $\mathrm{ER}(n…