User behavior based link prediction in online social networks

In recent years, online social networks such as Facebook, Twitter, LinkedIn are most popular visited sites over the internet. Presently, there is a great interest in understanding and studying the relationships among the users in social networks. Existing link prediction methods predicts the links based on the topological structure features and the node attribute features but overlook the benefit other features such as clicks, shares, likes, forwards and comments could bring to any social network. To address this gap we propose a link prediction method based on user actions with the post which includes clicks, shares, likes, forwards and comments. In this paper, we propose link prediction model combining user action metrics and topological structure metrics. The proposed metric can bridge the gap between the existing methods and propose a new metric for defining link prediction. This is a work in progress paper, further as future direction, implementation of the proposed metrics on standard datasets by suitably training the classifiers is the topic of investigation.