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A novel link prediction method for supervising transitivity process

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Abstract

Link prediction has become an important area in network analysis in recent years due to its theoretical and practical significance. In this paper, we present a similarity-based prediction method under simultaneous consideration of multiple information sources and the corresponding discrimination ability. We first propose a novel supervised transitivity similarity index (STSI), in which the likelihood ratio in the Bayesian theory is employed to supervise the transitivity process. Then, based on the proposed STSI, we design a supervised transitivity similarity algorithm (STSA) for predicting missing links. Finally, empirical experiments are conducted to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve a good performance, compared with other mainstream baselines.

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Acknowledgements

This work is supported in part by National Natural Science Foundation of China with Grant Nos. 71720107002, 71450009 and 61572459, the Beijing Municipal Education Commission Foundation of China (No. KM201810038001), and the Special Research Funds from the Quantitative Finance Research Center of School of Information, Capital University of Economics and Business.

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Authors and Affiliations

  1. School of Information, Capital University of Economics and Business, Beijing, China

    Cheng Jiang, Wei Chen & Jun Zhang

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  1. Cheng Jiang
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  2. Wei Chen
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  3. Jun Zhang
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Correspondence to Wei Chen.

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Jiang, C., Chen, W. & Zhang, J. A novel link prediction method for supervising transitivity process. Appl Intell 48, 4305–4316 (2018). https://doi.org/10.1007/s10489-018-1196-0

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