In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms
(e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how
symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture
can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The
proposed methodology determines the parameters for the interaction between nodes in the network by estimating a
multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and withinsubject
information in a multilevel framework. The resulting network architecture can subsequently be analyzed through
network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors.
We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to
variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the
network. Implications and extensions of the methodology are discussed.
