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Description: This data-driven study framed in the interactionist approach investigates the influence of social graph topology and peer interaction dynamics among foreign exchange students in Germany taking part in an intensive language course on both self-reported and objectively measured SLA outcomes. Applying the algorithms and metrics of computational social network analysis (SNA), we find that the best predictor of TL performance is reciprocal interactions in the language being acquired, that the proportion of output in the TL is a stronger predictor than input, a negative relationship between performance and interactions with same-L1 speakers, a significantly underperforming English native-speaker dominated cluster, more intense interactions taking place between students of different proficiency levels, as well as several other significant findings. Unlike previous Study Abroad social network research concentrating on i) the micro-level of individual learners’ egocentric networks, presenting an emic view only, and ii) primarily TL native-speaker contacts, we demonstrate how and why peer learner networks can be examined in their entirety, complementing an etic perspective. This study, constituting the first application of computational SNA to a complete learner network (sociogram), provides new insights into the link between social relations and second language acquisition, showing how social network configuration and peer interaction dynamics are stronger predictors of L2/L3/Lx performance than individual factors such as attitude or motivation, and offers a rigorous methodology for investigating the phenomenon. The data accompany the following publication: Paradowski, M.B., Cierpich-Kozieł, A., Chen, C.-C., & Ochab, J.K. (2022). How output outweighs input and interlocutors matter for study-abroad SLA: Computational Social Network Analysis of learner interactions. The Modern Language Journal, 106(4).