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Path-based estimation for link prediction

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A Correction to this article was published on 20 July 2021

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Abstract

Link prediction has received a great deal of attention from researchers. Most of the existing researches are based on the network topology but ignore the importance of its preference; for aggregating multiple pieces of information, they normally sum up them directly. In this paper, a path-based probabilistic model is proposed to estimate the potential connectivity between any two nodes. It takes carefully the effective influence of nodes and the dependency among paths between two fixed nodes into account. Furthermore, we formulate the connectivity of two inner-community nodes and that of two inter-community nodes. The qualitative analysis shows that the links between inner-community nodes are more likely to be predicted by the proposed model. The performance is verified on both the multi-barbell network and Lesmis network. Considering the proposed model’s practicability, we develop an algorithm that iterates over the adjacent matrix to simulate paths of different lengths, with the parameters automatically grid-searched. The results of the experiments show that the proposed model outperforms competitive methods.

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Notes

  1. All pictures of networks in this paper are drawn by Gephi which is a software for the visualization of graphs and networks (http://networkrepository.com/index.php). Fruchterman Reingold [32] is used to generate the network layout, and the nodes in a network are colored by their modularities.

  2. These real networks can be downloaded at http://networkrepository.com/index.php.

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Acknowledgements

The authors would like to thank Yayu Zhang, Furong Lu, Honghong Cheng, Jieting Wang and Junjie Ma for their insightful discussions. This work was supported by National Natural Science Foundation of China (nos. 61672332, 61322211, 61432011, 61872226 and U1435212), the Young Scientists Fund of the National Natural Science Foundation of China (Grant no. 61802238). Program for New Century Excellent Talents in University (no. NCET-12-1031), Program for the Outstanding Innovative Teams of Higher Learning Institutions of Shanxi, and Program for the Young San Jin Scholars of Shanxi, the Natural Science Foundation of Shanxi Province (no. 201701D121052), the Research Project Supported by Shanxi Scholarship Council of China (no. 2017023).

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Author notes

  1. Guoshuai Ma and Hongren Yan contributed equally to this work.

Authors and Affiliations

  1. Institute of Big Data Science and Industry, Shanxi University, Taiyuan, 030006, Shanxi, China

    Guoshuai Ma, Hongren Yan & Yuhua Qian

  2. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Guoshuai Ma, Hongren Yan & Yuhua Qian

  3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, Shanxi, China

    Yuhua Qian

  4. Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, 53211, USA

    Lingfeng Wang

  5. Department of Manufacture Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong

    Chuangyin Dang

  6. The School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhongying Zhao

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  1. Guoshuai Ma
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Ma, G., Yan, H., Qian, Y. et al. Path-based estimation for link prediction. Int. J. Mach. Learn. & Cyber. 12, 2443–2458 (2021). https://doi.org/10.1007/s13042-021-01312-w

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