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 qu

SUBMITTER: Zhang P 

PROVIDER: S-EPMC4702065 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature