How well do network models predict observations? On the importance of predictability in network models

By Jonas M. B. Haslbeck, Lourens J. Waldorp. Behavior Research Methods, 2016

532 citations recorded by the source corpus.

Network models are an increasingly popular way to abstract complex psychological phenomena. While studying the structure of network models has led to many important insights, little attention has been 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 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.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b5442976865ee8d0fd9a900fc9a9db35d404e4c5

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Citations in the corpus

mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, unknown): “For details see Haslbeck and Waldorp (2016).”
A Psychometric Network Perspective on the Validity and Validation of Personality Trait Questionnaires (2019, methodology): “More directly, researchers could square these same partial correlation coefficients to derive partial R2 values (or the residual variation explained by adding the variable to the network), which makes for more interpretable results (Haslbeck and Waldorp, 2018).”