Understanding the dual role of individual position in multidimensional social support networks and depression levels: Insights from a nomination-driven framework.

This study aims to investigate the relationship between the structural characteristics (e.g., single dimension friendship networks) and functional characteristics (multi-dimension) of social support networks and depressive symptoms among college students. Data were collected from 1784 students across six Chinese universities using questionnaire surveys and a nomination-based social network analysis approach. Friendship and social support networks (including four dimensions of support: appraisal, belonging, tangible, and self-esteem), were constructed to exploring relationships between network characteristics and depressive symptoms at both individual and class levels. The results indicate that greater integration and active participation in these networks are significantly linked to lower depression risks at both individual and class levels, underscoring the protective role of social connections. Yet, individuals with high betweenness centrality in networks demanding high support face increased depression risks, attributed to the stress of maintaining social cohesion and identity. Multilevel analysis further reveals that class network modularity is positively correlated with depressive symptoms and moderates the relationship between local clustering and depressive symptoms in high-burden social support networks, indicating that individuals in bridge positions or on the periphery of high-modularity networks may face increased risk of depression, potentially due to the lack of strong emotional support and social validation. These findings, by focusing on the characteristics of networks at both individual and group levels, lay a foundation for targeted intervention measures designed to optimize social support systems. They offer insights into mental health policies and practices among college students.