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Background Network analyses on psychopathological data focus on the networkstructure and its derivatives such as node centrality. One conclusion one candraw from centrality measures is that the node with the highest centrality islikely to be the node that is determined most by its neighboring nodes.However, centrality is a relative measure: knowing that a node is highlycentral gives no information about the extent to which it is determined by itsneighbors. Here we provide an absolute measure of determination (orcontrollability) of a node - its predictability. We introduce predictability,estimate the predictability of all nodes in 18 prior empirical network paperson psychopathology, and statistically relate it to centrality.  Methods We carried out a literature review and collected 25 datasets from 18published papers in the field (several mood and anxiety disorders, substanceabuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-artnet- work models to all datasets, and computed the predictability of all nodes.  Results Predictability was unrelated to sample size, moderately high in mostsymptom networks, and differed considerable both within and between datasets.Predictability was higher in community than clinical samples, highest for moodand anxiety disorders, and lowest for psychosis.  Conclusions Predictability is an important additional characterization ofsymptom networks because it gives an absolute measure of the controllability ofeach node. It allows conclusions about how self-determined a symptom networkis, and may help to inform intervention strategies. Limitations ofpredictability along with future directions are discussed.

How Predictable are Symptoms in Psychopathological Networks? A Reanalysis of 18 Published Datasets