Finding friends on a new site using minimum information

With the emergence of numerous social media sites, individuals, with their limited time, often face a dilemma of choosing a few sites over others. Users prefer more engaging sites, where they can find familiar faces such as friends, relatives, or colleagues. Link prediction methods help find friends using link or content information. Unfortunately, whenever users join any site, they have no friends or any content generated. In this case, sites have no chance other than recommending random influential users to individuals hoping that users by befriending them create sufficient information for link prediction techniques to recommend meaningful friends. In this study, by considering social forces that form friendships, namely, influence, homophily, and confounding, and by employing minimum information available for users, we demonstrate how one can significantly improve random predictions without link or content information. In addition, contrary to the common belief that similarity between individuals is the essence of forming friendships, we show that it is the similarity that one exhibits to the friends of another individual that plays a more decisive role in predicting their future friendship.