Detecting Friendship Within Dynamic Online Interaction Networks


  • Sears Merritt University of Colorado, Boulder
  • Abigail Jacobs University of Colorado, Boulder
  • Winter Mason Stevens Institute of Technology
  • Aaron Clauset University of Colorado, Boulder and Santa Fe Institute



social networks, friendship inference, temporal data, machine learning


In many complex social systems, the timing and frequency of interactions between individuals are observable but friendship ties are hidden. Here, we investigate the accuracy of multiple statistical features, based either purely on temporal interaction patterns or on the cooperative nature of the interactions, for automatically extracting latent social ties. Using self-reported friendship and non-friendship labels derived from an anonymous online survey, we learn highly accurate predictors for recovering hidden friendships within a massive online data set encompassing 18 billion interactions among 17 million individuals of the popular online game Halo: Reach. We find that periodicities in interaction time series are sufficient to correctly classify 95% of ties, even for casual users. These results clarify the nature of friendship in online social environments and suggest new opportunities and new privacy concerns for friendship-aware applications that do not require the disclosure of private friendship information.




How to Cite

Merritt, S., Jacobs, A., Mason, W., & Clauset, A. (2021). Detecting Friendship Within Dynamic Online Interaction Networks. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 380-389.