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网络中的标准化局部分类以及金融市场中的系统性风险
Mike K P So1, Anson S W Mak2, Jacky N L Chan1
1Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong.
PloS one
|October 5, 2023
概括
本研究引入了一个新的局部分类系数,以分析细分层面的网络结构. 这有助于识别局部模式,并跟踪动态网络中的系统性风险.
科学领域:
- 网络科学 网络科学
- 复杂系统分析 复杂系统分析
- 图形理论 图形理论
背景情况:
- 选择性对于理解网络异质性和弹性至关重要.
- 全球分类性测量可以掩盖混合网络中的本地模式.
- 需要局部系数来捕捉细粒度网络特征.
研究的目的:
- 提出一个标准化,规模独立的局部分类系数.
- 为了能够在实体层面上观察分类特征.
- 为了捕捉动态网络中的模式形成和溶解.
主要方法:
- 开发一种新的标准化局部分类系数.
- 应用系数来分析静态和动态网络结构.
- 使用系数来识别分类中心和跟踪系统性风险.
主要成果:
- 拟议的系数揭示了网络中的细粒度分类特征.
- 它成功地在静态网络中识别了 (dis) assortative 中枢.
- 该系数有效地追踪动态金融网络中的系统风险.
结论:
- 标准化的本地分类系数为网络结构提供了详细的视图.
- 它增强了对网络异质性和弹性的理解.
- 这种工具对于分析包括金融网络在内的复杂系统非常有价值.
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