Inferring Unobservable Inter-community Links in Large Social Networks

Social networks can be used to model social interactions between individuals. In many circumstances, not all interactions between individuals are observed. In such cases, a social network is constructed with the data that has been observed, as this is the best one can do. Recent research has attempted to predict future links in a social network, though this has proven a very challenging task. Rather than predicting future links, we propose an inference method for recovering the links in a social network that already exist but that have not been observed. In addition, our approach automatically identifies groups of individuals that form tight-knit communities and models the intra and inter-community interactions. At this higher level of abstraction and for a social network built frommobile phone calls, our method is able to accurately identify a subset of 10% of all community pairs where about 50% of the pairs have had unobserved communication between them, an improvement of about four times over a subset of the same size with randomly chosen pairs. To the best of our knowledge, this is the first method that infers links that exist but are unobservable in a phone call-based social network. In addition, we perform the inference at the community level, where the discovery of unobserved inter-community communication can provide further insight into the organizational structure of the social network andcan identify social groups that may share common interests.