Finding Similar Users in Facebook

Abstract

Online social networks are rapidly asserting themselves as popular services on the Web. A central point is to determine whether two distinct users can be considered similar, a crucial concept with interesting consequences on the possibility to accomplish targeted actions like, for example, political and social aggregations or commercial promotions. In this chapter we propose an approach in order to estimate the similarity of two users based on the knowledge of social ties (i.e., common friends and groups of users) existing among users, and the analysis of activities (i.e., social events) in which users are involved. For each of these indicators, we draw a local measure of user similarity which takes into account only their joint behaviours. After this, we consider the whole network of relationships among users along with local values of similarities and combine them to obtain a global measure of similarity. Such a computation is carried out by applying the Katz coefficient, a popular parameter introduced in Social Science research. Finally, similarity values produced for each social activity are merged into a unique value of similarity by applying linear regression.