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Link Prediction in Dynamic Networks Based on Topological and Historical Information

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Theoretical Computer Science (NCTCS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1944))

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Abstract

Many real-world networks are dynamic ones whose structure keeps changing over time. Link prediction in dynamic networks is more challenging and complex than that in static ones due to their dynamic nature. However, effectively using the information carried by dynamic networks can make notable enhancements to prediction accuracy. To solve the problem of dynamic network link prediction, this paper proposes a new supervised method, named THILP. This method treats link prediction as a regression problem. In this regard, both elaborate topological and historical features are extracted from multiple snapshots to represent node pairs, and the RandomForestRegressor algorithm is adopted to train a prediction model. Extensive experiments are executed on nine benchmark networks to investigate the effectiveness of the THILP method. The results show that THILP behaves remarkably better than baseline methods.

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Notes

  1. 1.

    https://www.python.org/.

  2. 2.

    http://networkx.github.io/.

  3. 3.

    https://scikit-learn.org/stable/.

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Acknowledgments

This study was supported in part by the Science and Technology Program of Gansu Province (Nos. 21JR7RA458 and 21ZD8RA008), and the Supercomputing Center of Lanzhou University.

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Erfei Jia, Dongwen Tian & Longjie Li

  2. State Grid Gansu Electric Power Company Material Company, Lanzhou, 73050, China

    Tian Nan

  3. Key Laboratory of Media Convergence Technology and Communication, Lanzhou, 730000, Gansu, China

    Longjie Li

Authors
  1. Erfei Jia
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  2. Dongwen Tian
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  3. Tian Nan
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  4. Longjie Li
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Correspondence to Longjie Li .

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Editors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Zhiping Cai

  2. University of Electronic Science and Technology, Chengdu, China

    Mingyu Xiao

  3. Chinese Academy of Sciences, Beijing, China

    Jialin Zhang

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Jia, E., Tian, D., Nan, T., Li, L. (2024). Link Prediction in Dynamic Networks Based on Topological and Historical Information. In: Cai, Z., Xiao, M., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2023. Communications in Computer and Information Science, vol 1944. Springer, Singapore. https://doi.org/10.1007/978-981-99-7743-7_13

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  • DOI: https://doi.org/10.1007/978-981-99-7743-7_13

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