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English(EN) CDS: Counterfactual Directionality Score for Structured Interventions in Spatial Graphs

新框架量化空间图中的方向性影响

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个名为反事实方向性得分（CDS）的新框架，用于量化图模型中节点种群之间的方向性影响，特别适用于空间生物系统。该方法训练邻域影响模型（NIM），并应用结构化反事实干预来评估目标扰动如何影响预测的节点状态。在合成数据和空间转录组学上的实验表明，CDS能够准确识别方向性影响并提供可靠的不确定性估计。 AI

影响 该框架通过提供一种更原则性的方法来理解组件之间的因果关系，有望改进复杂生物系统的分析。

排序理由 该集群包含一篇详细介绍图模型新框架和方法的学术论文。

在 arXiv cs.LG 阅读 →

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新框架量化空间图中的方向性影响

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Humaira Anzum, Md Ishtyaq Mahmud, Jagan Mohan Reddy Dwarampudi, Tania Banerjee ·

    CDS：空间图结构化干预中的反事实方向性得分

    arXiv:2607.13508v1 Announce Type: new Abstract: Quantifying directional influence between node populations is a fundamental problem in graph-based modeling, particularly in spatial biological systems where cell-cell interactions shape functional outcomes. Existing approaches base…

  2. arXiv cs.LG TIER_1 English(EN) · Tania Banerjee ·

    CDS：空间图结构化干预中的反事实方向性得分

    Quantifying directional influence between node populations is a fundamental problem in graph-based modeling, particularly in spatial biological systems where cell-cell interactions shape functional outcomes. Existing approaches based on attention, attribution, or correlation capt…

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