Link Prediction in the Twitter Mention Network: Impacts of Local Structure and Similarity of Interest

The creation of social ties is driven by several factors which can arguably be related to individual preferences and to the common social environment of individuals. Effects of homophily and triadic closure mechanisms are claimed to be important in terms of initiating new social interactions and in turn to shape the global social structure. This way they eventually provide some potential to predict the creation of social ties between disconnected people sharing common friends or common subjects of interest. In this paper we analyze a large Twitter data corpus and quantify similarities between people by considering the set of their common friends and the set of their commonly shared hashtags in order to predict mention links among them. We show that these similarity measures are correlated among connected people and that the combination of contextual and local structural features provides better predictions as compared to cases where they are considered separately. These results help us to better understand the evolution of egocentric and global social networks and provide advances in the design of better recommendation systems and resource allocation plans.