Before revision:  In this paper, we propose a new evaluation scheme for link prediction algorithms, i.e., link prediction with random sampling . We use this method to evaluate the performance of twelve link predictors on ten real-world networks of different contexts and scales .
After revision:  In this paper, we propose a new evaluation paradigm that is sampling unobserved links to address this problem. First, we demonstrate that the proposed paradigm is feasible in theory. Then, we perform extensive evaluation experiments in real-world networks of different contexts and scales .