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Meta-path based heterogeneous combat network link prediction

Author

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  • Li, Jichao
  • Ge, Bingfeng
  • Yang, Kewei
  • Chen, Yingwu
  • Tan, Yuejin

Abstract

The combat system-of-systems in high-tech informative warfare, composed of many interconnected combat systems of different types, can be regarded as a type of complex heterogeneous network. Link prediction for heterogeneous combat networks (HCNs) is of significant military value, as it facilitates reconfiguring combat networks to represent the complex real-world network topology as appropriate with observed information. This paper proposes a novel integrated methodology framework called HCNMP (HCN link prediction based on meta-path) to predict multiple types of links simultaneously for an HCN. More specifically, the concept of HCN meta-paths is introduced, through which the HCNMP can accumulate information by extracting different features of HCN links for all the six defined types. Next, an HCN link prediction model, based on meta-path features, is built to predict all types of links of the HCN simultaneously. Then, the solution algorithm for the HCN link prediction model is proposed, in which the prediction results are obtained by iteratively updating with the newly predicted results until the results in the HCN converge or reach a certain maximum iteration number. Finally, numerical experiments on the dataset of a real HCN are conducted to demonstrate the feasibility and effectiveness of the proposed HCNMP, in comparison with 30 baseline methods. The results show that the performance of the HCNMP is superior to those of the baseline methods.

Suggested Citation

  • Li, Jichao & Ge, Bingfeng & Yang, Kewei & Chen, Yingwu & Tan, Yuejin, 2017. "Meta-path based heterogeneous combat network link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 507-523.
  • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:507-523
    DOI: 10.1016/j.physa.2017.04.126
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    References listed on IDEAS

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    Cited by:

    1. Li, Ji-chao & Zhao, Dan-ling & Ge, Bing-Feng & Yang, Ke-Wei & Chen, Ying-Wu, 2018. "A link prediction method for heterogeneous networks based on BP neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 1-17.
    2. Wu, Jiehua & Shen, Jing & Zhou, Bei & Zhang, Xiayan & Huang, Bohuai, 2019. "General link prediction with influential node identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 996-1007.
    3. Shakibian, Hadi & Charkari, Nasrollah Moghadam, 2018. "Statistical similarity measures for link prediction in heterogeneous complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 248-263.
    4. Wang, Ruby W. & Wei, Shelia X. & Ye, Fred Y., 2021. "Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength," Journal of Informetrics, Elsevier, vol. 15(3).
    5. Jichao Li & Bingfeng Ge & Jiang Jiang & Kewei Yang & Yingwu Chen, 2020. "High-end weapon equipment portfolio selection based on a heterogeneous network model," Journal of Global Optimization, Springer, vol. 78(4), pages 743-761, December.

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