Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus nonsocial network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies. Figure 3. Patterns of functional connectivity in the brain are distinct for social network learning compared to a non-social control condition. (A) Functional connectivity matrix showing edges with significantly different weights for the social network condition (upper triangle in red) versus the non-social control condition (lower triangle in blue). Significance was assessed after an FDR correction at p<0.05 over edges. (B) Whole-brain mapshowing regions with significantly different strength z-scores for trials in the social network condition (red) versus in the non-social control condition (blue). Significance was assessed after an FDR correction at p<0.05 over brain regions. (C) Bar graph showing percentage of network hubs identified in each cognitive system for the social network condition versus the non-social control condition, relative to the percentage identified in a non-parametric null model (gray box plots). (D) By averaging edge weights both within and between putative functional systems, we constructed a system-level connectivity matrix. Here we display that matrix showing systems with significantly different connectivity for the social network condition (red) versus the non-social control condition (blue). Signifi...