Comparing NIRA and Traditional Network Approaches: A Study Case With Antisocial Personality Disorder Traits.

Abstract

Objective: This study explores the NodeIdentifyR algorithm (NIRA) as a novel network analysis method for examining Antisocial Personality Disorder (ASPD) traits.

Methods: Using a sample of 2230 Brazilian adults (aged 18-73 years) who responded to ASPD-related factors of the Personality Inventory for DSM-5 (PID-5), we applied NIRA to an ASPD network and compared its results with traditional network analysis methods.

Results: Our findings revealed that deceitfulness emerged as the most central trait across both methodologies. NIRA provided additional insights, indicating that simulated decreases in the likelihood of irresponsibility reduced the presence of other traits, while a simulated increase in deceitfulness amplified the likelihood of other ASPD pathological traits.

Conclusions: Our results suggest that traditional network centrality measures converge with NIRA's simulated increase results, but NIRA's simulated decrease provides additional information not captured by traditional centrality estimates. We recommend further research to validate these findings across different psychopathologies and refine NIRA use in clinical settings. The insights from this study could serve as a foundation for developing targeted interventions and enhancing our understanding of ASPD trait dynamics.