Network analysis: An overview for mental health research

Abstract

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Keywords: network analysis; network modeling; network psychometrics; network psychopathology.

FIGURE 1

The eight possible structures for a three‐node network with their prior and posterior probabilities.

FIGURE 2

Directed and undirected graphical separation, illustrated using all possible patterns of three nodes connected by two edges.

FIGURE 3

An example of a Bayesian network representing potential causal relationships in psychopathology. The directed edges illustrate hypothesized causal influences among various symptoms, such as “Anxiety” leading to “Insomnia” and “Depression” influencing “Withdrawal”. The Markov blanket of “Depression”, consisting of “Anxiety” (parent node), “Insomnia” (spouse), “Fatigue” and “Withdrawal” (children), is highlighted in gray. The local distributions defined in Equation (2) take the form shown below the network.