GGMNIRA as a Gaussian Graphical Model Extension of the NodeIdentifyR Algorithm for Projected Node Importance
Abstract
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".
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BibTeX
@misc{wu2026ggmnira,
title = {{GGMNIRA as a Gaussian Graphical Model Extension of the NodeIdentifyR Algorithm for Projected Node Importance}},
author = {Yiming Wu and Fei Wang and Hongyun Liu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2607.00915},
eprint = {2607.00915},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.00915},
}
RIS
TY - UNPB
TI - GGMNIRA as a Gaussian Graphical Model Extension of the NodeIdentifyR Algorithm for Projected Node Importance
AU - Yiming Wu
AU - Fei Wang
AU - Hongyun Liu
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2607.00915
UR - https://arxiv.org/abs/2607.00915
AB - 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".
ER -