Predicting interactions in online social networks: an experiment in Second Life

Abstract

Although considerable amount of work has been conducted recently of how to predict links between users in online social media, studies exploiting different kinds of knowledge sources for the link prediction problem are rare. In this paper latest results of a project are presented that studies the extent to which interactions -- in our case directed and bi-directed message communication -- between users in online social networks can be predicted by looking at features obtained from social network and position data. To that end, we conducted two experiments in the virtual world of Second Life. As our results reveal, position data features are a great source to predict interacts between users in online social networks and outperform social network features significantly. However, if we try to predict reciprocal message communication between users, social network features seem to be superior.