The probabilistic dependency patterns between interpersonal emotion regulation and prosocial behavior in Chinese adolescents: insights from Gaussian Graphical and Bayesian Network Models

Interpersonal emotion regulation (IER) and prosocial behavior are critical constructs in adolescent socio-emotional development, yet their complex interplay remains poorly understood. This study employed Gaussian Graphical Model (GGM) and Bayesian network analysis to characterize the network structure and probabilistic dependency patterns among four IER strategies and six prosocial behavior tendencies in a sample of Chinese vocational school adolescents. A cross-sectional sample of 2,842 adolescents (M age = 15.49 years, SD = 1.13; 57.8% female) from 40 vocational schools completed the Interpersonal Emotion Regulation Questionnaire and the Prosocial Behavior Tendencies Measure. GGM identified a regularized network of 10 nodes and 33 edges, in which enhancement of positive affect exhibited the highest bridge centrality, descriptively serving as the most prominent hub connecting IER to emotional and anonymous prosocial behavior tendencies, while perspective-taking and social modeling were negatively associated with anonymous prosocial behavior. Bayesian network analysis identified probabilistic dependency patterns consistent with conditional dependencies between IER and prosocial behavior, including a probable upstream role of social modeling for anonymous prosocial behavior and convergent dependencies from prosocial tendencies onto enhancement of positive affect. Given the cross-sectional design, these patterns are hypothesis-generating rather than causal. Overall, IER and prosocial behavior are closely interconnected at the level of statistical dependence, with enhancement of positive affect functioning as the most prominent descriptive bridge node. Affect-centered strategies represent a plausible hypothesis for promoting adolescent prosocial development and should be tested in future experimental and longitudinal work.