title: Measuring the robustness of link prediction algorithms under noisy environment.
abstract: Link prediction in complex networks is to estimate the likelihood of two nodes to interact with each other in the future. As this problem has applications in a large number of real systems, many link prediction methods have been proposed. However, the validation of these methods is so far mainly conducted in the assumed noise-free networks. Therefore, we still miss a clear understanding of how the prediction results would be affected if the observed network data is no longer accurate. In this paper, we comprehensively study the robustness of the existing link prediction algorithms in the real networks where some links are missing, fake or swapped with other links. We find that missing links are more destructive than fake and swapped links for prediction accuracy. An index is proposed to quantify the robustness of the link prediction methods. Among the twenty-two studied link prediction methods, we find that though some methods have low prediction accuracy, they tend to perform reliably in the "noisy" environment.
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Peng
authors.initials: P
authors.lastname: Zhang
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wang
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Futian
authors.initials: F
authors.lastname: Wang
authors.affiliation: School of Systems Science, Beijing Normal University, Beijing 100875, P.R. China.
authors.forename: An
authors.initials: A
authors.lastname: Zeng
authors.affiliation: School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, P.R. China.
authors.forename: Jinghua
authors.initials: J
authors.lastname: Xiao