发表机构
Faculty of Psychology, Beijing Normal University; State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences; Department of Psychology, University of Chinese Academy of Sciences（北京师范大学心理学部; 中国科学院心理研究所认知科学与心理健康重点实验室; 中国科学院大学心理学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出GGMNIRA算法，通过操纵节点条件均值并利用KL散度量化网络分布变化，将模拟操纵逻辑扩展至连续和序数数据，并开发了相关稳定性系数和自助法差异检验。
AI 中文摘要
科学摘要：在心理网络分析中，中心性指标常用于评估网络内节点的重要性。然而，中心性仅捕捉节点的静态拓扑位置，且缺乏充分的理论依据假设其反映节点对网络动态的影响。节点识别算法（NIRA）通过在Ising模型内系统地对节点截距进行模拟操纵来评估节点的预测重要性，但该算法仅限于二元数据，且操纵参数在精神病理学背景之外缺乏清晰的理论意义。为解决这些限制，我们提出了高斯图模型节点识别算法（GGMNIRA），该算法操纵节点的条件均值，并使用Kullback-Leibler（KL）散度量化操纵前后网络分布的变化，从而将这种模拟操纵逻辑扩展到适用于连续和序数数据的高斯图模型框架。围绕该算法，我们进一步开发了KL散度的相关稳定性系数和非参数自助法差异检验，并通过模拟研究建立了相应的解释阈值。该框架还扩展到了桥接高斯图模型和调节高斯图模型，使其能够应用于多结构共病网络以及涉及调节效应的情境。所有方法均在R包“GGMNIRA”中实现。
英文摘要
The NodeIdentifyR Algorithm (NIRA) has been increasingly applied in psychological network research as a simulated-manipulation approach for comparing the network-level impact associated with different nodes. However, NIRA is restricted to binary variables, and the theoretical interpretation of the manipulated node intercept is primarily grounded in the symptom-activation context of psychopathology. To address these limitations, we extend NIRA to Gaussian graphical model and propose the Gaussian Graphical Model NodeIdentifyR Algorithm (GGMNIRA). GGMNIRA systematically manipulates the conditional mean of each node and uses Kullback--Leibler (KL) divergence to quantify the resulting change in the joint network distribution. Because the conditional mean has a meaningful interpretation for variables across different areas of psychology, GGMNIRA can be applied to a broader range of psychological research contexts. We also developed complementary procedures for evaluating the uncertainty of GGMNIRA results, including a correlation-stability coefficient and a nonparametric bootstrap difference test for KL divergence. In addition, GGMNIRA was extended to bridge Gaussian graphical model, allowing simulated manipulation to be conducted at both the node and construct levels in multi-construct networks. All methods are implemented in the R package "GGMNIRA".
