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Evolutionary Method of Heterogeneous Combat Network Based on Link Prediction.

Shaoming Qiu1, Fen Chen1, Yahui Wang1

  • 1Communication and Network Laboratory, Dalian University, Dalian 116622, China.

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|May 27, 2023
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Summary
This summary is machine-generated.

This study introduces a new method for analyzing heterogeneous combat networks (HCNs) evolution. The proposed evolutionary method enhances operational capabilities and network realism compared to existing approaches.

Keywords:
heterogeneous combat networks (HCNs)link predictionoperational capability

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Area of Science:

  • Network science
  • Computer science
  • Military science

Background:

  • Research on heterogeneous combat networks (HCNs) evolution often overlooks topological impacts on operational capabilities.
  • Link prediction offers a standardized comparison for network evolution mechanisms.

Purpose of the Study:

  • To investigate the evolution of HCNs by focusing on network topology changes and their effect on operational capabilities.
  • To propose and validate a novel link prediction index and an evolutionary method for HCNs.

Main Methods:

  • Developed a link prediction index based on frequent subgraphs (LPFS) tailored for HCN characteristics.
  • Validated LPFS against 26 baseline methods on a real-world combat network.
  • Proposed a heterogeneous combat network evolution (HCNE) method and compared its performance against random and preferential evolution models.

Main Results:

  • The proposed LPFS method significantly outperformed 26 baseline link prediction techniques.
  • The HCNE method demonstrated superior performance in enhancing operational capabilities compared to random and preferential evolution.
  • Experimental results indicate that the evolved networks exhibit greater consistency with real-world network characteristics.

Conclusions:

  • The LPFS index provides a robust standard for evaluating HCN evolution mechanisms.
  • The HCNE method effectively improves the operational capabilities of combat networks.
  • The study highlights the importance of topology in HCN evolution and provides a method for generating more realistic network structures.