基于链接预测的异质战斗网络的进化方法
Shaoming Qiu1, Fen Chen1, Yahui Wang1
1Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究引入了一种分析异质战斗网络 (HCN) 演变的新方法. 与现有方法相比,提出的进化方法增强了运营能力和网络现实性.
科学领域:
- 网络科学 网络科学
- 计算机科学 计算机科学
- 军事科学 军事科学
背景情况:
- 关于异质战斗网络 (HCNs) 演变的研究往往忽视了对作战能力的拓影响.
- 链接预测为网络演变机制提供了标准化的比较.
研究的目的:
- 通过关注网络拓变化及其对运营能力的影响,研究HCN的演变.
- 提出和验证一种新的链接预测指数和HCNs的进化方法.
主要方法:
- 开发了一个基于频率子图 (LPFS) 的链接预测指数,适合HCN特征.
- 在现实战斗网络上对26种基线方法进行验证的LPFS.
- 提出了一种异质战斗网络进化 (HCNE) 方法,并将其性能与随机和优先进化模型进行了比较.
主要成果:
- 拟议的LPFS方法显著优于26种基线链接预测技术.
- 与随机和优先演变相比,HCNE方法在提高运营能力方面表现优越.
- 实验结果表明,进化的网络与现实世界的网络特征具有更大的一致性.
结论:
- LPFS指数为评估HCN进化机制提供了一个强大的标准.
- 该HCNE方法有效地提高了战斗网络的作战能力.
- 该研究强调了拓在HCN演变中的重要性,并提供了一种生成更现实的网络结构的方法.
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