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| Link Prediction Algorithm Based on Biased Random Walk with Restart |
| Ly Ya′nan, HAN Hua, JIA Chengfeng, QU Qianqian
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| School of Science,Wuhan University of Technology, Wuhan 430070, China |
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Abstract In link prediction, the similarity indices based on random walk process often set the probability of particles transferring to adjacent nodes to be equal, but neglecting the influence of node degree on the transition probability. To save this problem, a link prediction algorithm of biased random walk with restart is proposed. Firstly, we redefine the transfer probability of particles by referring to biased random walk. Then we apply it to the random walk with restart to explore the effect of node degree on the transfer of particles. Finally, on the basis of biased random walk,the proposed index is compared with six classical similarity indices.The experimental results of six real data sets show that the prediction algorithm of biased random walk with restart has higher prediction accuracy than that the unbiased one, and is better than other similarity indices.
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Published: 16 May 2019
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