BackgroundDepression remains among the most prevalent mental disorders, and it severely affects daily functioning and quality of life. There has been extensive research reporting on the impact of social relationships on depression, but much of this research has only considered isolated aspects of relationships. This study derived social network types based on the multiple components of social relationships, and then investigated their effects on depressive symptoms.MethodsUsing samples of 620 adults (Mage= 53.52), Latent Profile Analysis (LPA) was conducted to uncover network types based on the structural (network size, contact frequency, marital status, and social engagement), functional (levels of support and conflict), and qualitative (satisfaction with relationships) aspects of social relationships. Multiple regressions were used to test if distinct network types directly impact on depressive symptoms and whether network types moderate the association of loneliness (perceived social isolation) with depressive symptoms.ResultsLPA identified four distinct network types (diverse, family-focused,friend-focused, andrestricted) and there were significant differences in depressive symptoms among four network types. Analysis using the BCH method showed that individuals in therestrictednetwork type had the highest depressive symptoms, followed in order by individuals in thefriend-focused,family-focused, anddiversenetwork types. Regression results further indicated that an individual’s network network type membership was significantly associated with depressive symptoms, and being in thediverseandfamily-focusednetwork types alleviated the negative effect of loneliness on depressive symptoms.ConclusionThe results suggest that both quantitative and qualitative aspects of social relationships are important in buffering against the adverse effect of loneliness on depressive symptoms. These findings underscore the utility of taking a multi-dimensional approach to uncover heterogeneity in the social networks of adults and their implications on depression.