Robustness of Link-Prediction Algorithm Based on Similarity and Application to Biological Networks
Liang Wang, Ke Hu and Yi Tang
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.
Keywords: Biological networks, link-prediction algorithm, link weighting, robustness.
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