Affiliation: Department of Physics and Key Laboratory of Intelligent Computing & Information Processing (Xiangtan University) Ministry of Education, Xiangtan University, Xiangtan 411105, Hunan, China.
Many algorithms have been proposed to predict missing links in a variety of real networks. Emphasis is put on raising both accuracy and efficiency of these algorithms. However, less attention is paid to their robustness against either noise or irrationality of a link which exists in almost all of real networks. In this paper, we investigate the robustness of several typical node-similarity-based algorithms and find that these algorithms are sensitive to the strength of noise. Moreover, we find that it also depends on the structure properties of networks, especially on network efficiency, clustering coefficient and average degree. In addition, we make an attempt to enhance the robustness by using link weighting method to transform un-weighted network into weighted one and then making use of weights of links to characterize their reliability. The result shows that proper link weighting scheme can enhance both robustness and accuracy of these algorithms significantly in biological networks.