在基于库尔巴克-莱布勒模型的复杂网络中,在邻里范围内找到有影响力的节点
Guan Wang1, Zejun Sun2, Tianqin Wang3
1School of Information Engineering, Pingdingshan University, Pingdingshan, 467000, China. wangguan072@163.com.
在复杂网络中识别有影响力的节点是联合学习可靠性的关键. 拟议的KLN算法有效地使用Kullback-Leibler分歧评估节点重要性,以更好地优化系统.
科学领域:
- 网络安全 网络安全
- 复杂网络分析 复杂网络分析
- 机器学习 机器学习
背景情况:
- 联合学习涉及分布式设备之间广泛的信息交换.
- 识别有影响力的节点对于优化联合学习系统的可靠性至关重要.
研究的目的:
- 提出一种用于识别复杂网络中具有影响力的节点的新方法.
- 提高联合学习系统的可靠性和效率.
主要方法:
- 邻近 (KLN) 算法内的库尔巴克-莱布勒分歧模拟节点失败.
- KLN使用KL分歧和网络属性来量化信息损失,以评估节点的重要性.
主要成果:
- 与其他11个算法相比,KLN在各种网络尺度上展示了卓越的准确性和适用性.
- 使用SIR模型和现实世界流行病网络的实验验证证证了KLN的有效性.
结论:
- KLN算法提供了一种有效的方法来评估复杂网络中节点的重要性.
- 这些发现支持开发针对网络安全和疫情预防的有针对性的管理和控制策略.
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