Link Prediction in Social Networks Using Information Flow via Active Links

Abstract

Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focused on predicting links in social networks using information flow via active links. The information flow heavily depends on link activeness. The links become active if the interactions happen frequently and recently with respect to the current time. The time stamps of the interactions or links provide vital information for determining the activeness of the links. In the present paper, we introduced a new algorithm, referred to as T_Flow, that captures the important aspects of information flow via active links in social networks. We tested T_Flow with two social network data sets, namely, a data set extracted from Facebook friendship network and a coauthorship network data set extracted from ePrint archives. We compare the link prediction performances of T_Flow with the previous method PropFlow. The results of T_Flow method revealed a notable improvement in link prediction for facebook data and significant improvement in link prediction for coauthorship data.