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 .
Link prediction has aroused extensive attention since it can both discover hidden connections and predict future links in the networks. Many unsupervised link prediction algorithms have been proposed to find these links in a variety of networks . However, there is an evaluation conundrum in unsupervised link prediction. Unobserved links heavily outnumber observed links in large networks , so it is unrealistic to quantify the existence likelihood of all unobserved links to evaluate these algorithms . 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 sizes . The results show that the performance of similarity-based link prediction algorithms is highly stable even at a low sampling ratio in large networks , and the evaluation time degradation caused by sampling is striking . Our findings have broad implications for link prediction .
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The evaluation of a link prediction algorithm requires to estimate the possibility of the existence of all unobserved
Link prediction has aroused extensive attention since it can both discover hidden connections and predict future links in the networks. Many unsupervised link prediction algorithms have been proposed to find these
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network
variety of networks
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the number of unobserved links grows exponentially with the increase of the number of nodes, which limits link prediction
there is an evaluation conundrum in unsupervised link prediction. Unobserved links heavily outnumber observed links
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, so it is unrealistic to quantify the existence likelihood of all unobserved links to evaluate these algorithms
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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
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
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scales
sizes
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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)
of similarity-based link prediction algorithms is highly stable even at a low sampling ratio
A
, and the evaluation time degradation caused by sampling is striking
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show that the proposed scheme is a fast and effective evaluation method
have broad implications for link prediction