  Network models are an increasingly popular way to abstract complex
psychological phenomena. While the study of the structure of network models has
led to many important insights, little attention is paid to how well they
predict observations. This is despite the fact that predictability is crucial
for judging the practical relevance of edges: for instance in clinical
practice, predictability of a symptom indicates whether a an intervention on
that symptom through the symptom network is promising. We close this
methodological gap by introducing nodewise predictability, which quantifies how
well a given node can be predicted by all other nodes it is connected to in the
network. In addition, we provide fully reproducible code examples of how to
compute and visualize nodewise predictability both for cross-sectional and
time-series data.
