The nodes in networks can often take many forms: people in social networks, places in transportation networks, or cities in urban networks. In "psychological networks" the nodes are variables (e.g., beliefs, symptoms, or traits), and the edges are the statistical associations (e.g., partial correlations) among them in a sample of people. This type of network has become popular to study in psychology, however this might not be a good idea:
There are already methods for studying statistical associations among a set of variables.
Metrics developed for social networks or flow networks may not make sense in this context.
The edges are estimated from non-network data, not directly observed.
But, one overarching problem for this kind of network is the boundary specification problem. A network's structure is known to be distorted if nodes are missing. Because there is no way for a psychological network to include all beliefs or all traits, it will always be distorted.
Neal, Z. P., and Neal, J. W. (2023). Out of bounds? The boundary specification problem for centrality in psychological networks. Psychological Methods, 18, 179-188. https://doi.org/10.1037/met0000426 (pre-print available at https://psyarxiv.com/nz6k3)
Neal, Z. P., Forbes, M. K., Neal, J. W., Brusco, M., Krueger, R., Markon, K. E., Steinley, D., Wasserman, S., and Wright, A. G. (2022). Critiques of network analysis of multivariate data in psychological science. Nature Reviews Method Primers. (preprint: https://psyarxiv.com/jqs3n/; annotated bibliography: https://psyarxiv.com/ke6qn/)