Simulating Node Manipulations in Gaussian Graphical Models: The GGMNIRA Framework for Continuous and Ordinal Psychological Network Data

In psychological network analysis, centrality indices are commonly used to evaluate the importance of nodes within a network.

However, centrality only captures the static topological position of a node, and there is no sufficient theoretical justification for assuming that it reflects a node's influence on network dynamics.

The NodeIdentifyR Algorithm (NIRA) offers an alternative by systematically applying simulated manipulations to node intercepts within the Ising model to evaluate nodes' projected importance, but this algorithm is restricted to binary data, and the manipulated parameter lacks a clear theoretical meaning outside the context of psychopathology.

To address these limitations, we propose the Gaussian Graphical Model NodeIdentifyR Algorithm (GGMNIRA), which manipulates a node's conditional mean and uses Kullback-Leibler (KL) divergence to quantify the change in network distribution before and after manipulation, thereby extending this simulated manipulation logic to the Gaussian graphical model framework, which is applicable to continuous and ordinal data.

Around this algorithm, we further developed a correlation stability coefficient and a nonparametric bootstrap difference test for KL divergence, with corresponding interpretive thresholds established through simulation studies.

The framework was also extended to bridge Gaussian graphical models and moderated Gaussian graphical models, enabling its application to multi-construct comorbidity networks and to contexts involving moderation effects.

All methods are implemented in the R package "GGMNIRA".