  Many algorithms have been proposed to predict missing links in a variety of
real networks. These studies focus on mainly both accuracy and efficiency of
these algorithms. However, little attention is paid to their robustness against
either noise or irrationality of a link existing 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 networks'
structure properties, 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 to weighted one
and then make 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 while it
brings little computational effort.
