1 research outputs found
Fast Evaluation of Link Prediction by Random Sampling of Unobserved Links
The evaluation of a link prediction algorithm requires to estimate the
possibility of the existence of all unobserved links in a network. However, the
number of unobserved links grows exponentially with the increase of the number
of nodes, which limits link prediction in large networks. 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. 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. Our findings show
that the proposed scheme is a fast and effective evaluation method