  In many complex social systems, the timing and frequency of interactions
between individuals are observable but friendship ties are hidden. Recovering
these hidden ties, particularly for casual users who are relatively less
active, would enable a wide variety of friendship-aware applications in domains
where labeled data are often unavailable, including online advertising and
national security. 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 the accuracy of many features improves as more data
accumulates, and cooperative features are generally reliable. However,
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.
