What's strength centrality got to do with it? Examining the stability of central symptoms across symptom ensembles and time in idiographic networks.

Abstract

Network analysis is a popular method researchers use to characterize the structure of psychopathology and inform personalized treatments. Typically, applied researchers, based on network theory, interpret symptoms with the highest strength centrality as most important to network structure and represent amenable treatment targets. This study examines the stability of strength centrality in idiographic networks in a sample of participants with eating disorders (N = 26, 90-day assessment, M = 356.00 observations per person) and a second sample of participants with social anxiety disorder (N = 42, 30-day assessment, M = 201.90 observations per person). We estimated idiographic networks using three different item-inclusion approaches and accounted for time using a "sliding window" method (e.g., Window 1 = data from Days 1-15, Window 2 = data from Days 2-16). Items included in networks were selected in three ways: default networks (six items with the highest means at Window 1), changing means networks (six items with the highest means at each respective Window), and random ensembles (random combinations of any six items across all sliding windows). In both samples, we found that the most central symptom in the default network was central in less than half of idiographic changing means networks (maximum = 29.41% of networks). Our results show that node strength centrality estimates are sensitive to item ensemble and temporal effects. We discuss implications concerning inferences assigned to strength centrality given the frequency at which strength centrality changes and future efforts developing network-informed personalized treatment. (PsycInfo Database Record (c) 2025 APA, all rights reserved).