走向数字双胞胎导向的复杂网络系统:引入异质节点特征和交互规则
Jiaqi Wen1, Bogdan Gabrys1, Katarzyna Musial1
1Complex Adaptive Systems, Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia.
PloS one
|January 2, 2024
概括
本研究介绍了一个数字双胞胎导向复杂网络系统 (DT-CNSs) 框架来建模社交网络. 研究结果显示,针对高风险节点的有针对性的干预措施可以在流行病期间提高灾难适应能力.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 计算社会科学 计算社会科学
背景情况:
- 数字双胞胎导向复杂网络系统 (DT-CNS) 需要强大的建模框架.
- 现实世界的社交网络表现出复杂的特征和相互作用规则,影响系统动态.
研究的目的:
- 为DT-CNSs提出一个可扩展的建模框架.
- 创建忠实地代表现实世界的社会系统的网络.
- 在流行病爆发期间调查社交网络中的灾难适应能力.
主要方法:
- 开发了一个建模框架,专注于节点特征和交互规则.
- 在不同复杂度的DT-CNS进行了基于模拟的实验.
- 在社交网络中分析了流行病的传播和感染的发生.
主要成果:
- 结构和动态复杂性影响网络增长和流行病的传播.
- 具有首选特征的节点具有更高的感染风险.
- 针对高风险节点的缓解政策对于抗灾能力至关重要.
结论:
- 拟议的DT-CNSs框架有效地模拟了社交网络.
- 了解节点特征和交互规则是网络弹性的关键.
- 有针对性的干预措施对于有效的流行病控制和灾害管理至关重要.
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