整合本地和全球信息,以确定复杂网络中的有影响力的节点
Mohd Fariduddin Mukhtar1, Zuraida Abal Abas2, Azhari Samsu Baharuddin3
1Universiti Teknikal Malaysia Melaka, 76100, Durian Tunggal, Malaysia.
Scientific reports
|July 14, 2023
概括
我们介绍了混合GSM (H-GSM),这是一种结合K-shell分解和Degree Centrality的新方法,可以比现有模型更准确地识别复杂网络中的有影响力的节点.
科学领域:
- 网络科学 网络科学
- 计算社会科学 计算社会科学
背景情况:
- 集中性分析对于网络研究至关重要,但缺乏将独特信息与各种措施区分开来的方法.
- 像全球结构模型 (GSM) 这样的现有模型很难有效地区分节点的重要性.
研究的目的:
- 开发一种改进的方法来识别复杂网络中的有影响力的节点.
- 通过结合既有的中心性技术来提高网络分析的精度.
主要方法:
- 通过整合K-shell分解和Degree Centrality提出的混合GSM (H-GSM).
- 在六个现实网络上使用SIR模型评估H-GSM性能,以模拟传播过程.
主要成果:
- 与其他方法相比,H-GSM表现出优越的性能.
- 在计算复杂性,节点区分和准确性方面取得了更好的结果.
- 有效地描述了节点撞击,比GSM更准确.
结论:
- 混合GSM (H-GSM) 是一种有效和准确的方法来识别有影响力的节点.
- 整合K-shell分解和Degree Centrality在网络分析中提供了显著的优势.
- H-GSM提供了对复杂系统中节点影响的更细致的理解.
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