Before revision:  The results show that the performance ranking of these algorithms is not affected by randomly sampling a very small part from unobserved links for experiments, whether using AUC or the precision metric. Moreover, this sampling method can reduce the computational complexity for the evaluation of link prediction algorithms from O(n^2) to O(n) in large networks  .
After revision:  The results show that the performance of similarity-based link prediction algorithms is highly stable even at a low sampling ratio in large networks .