将机械网络模型转换为概率模型的框架
Ravi Goyal1, Victor De Gruttola2, Jukka-Pekka Onnela3
1Division of Infectious Diseases and Global Public, Health, University of California San Diego, 9500 Gilman Drive, La Jolla, CA USA.
概括
本研究介绍了将机械网络模型 (MNM) 转换为概率网络模型 (PNM) 的框架. 这允许更好地比较和分析由不同模型生成的网络属性.
科学领域:
- 网络科学 网络科学
- 计算建模计算建模
- 系统生物学 系统生物学
背景情况:
- 存在两个主要的网络建模方法:机械和概率.
- 机械模型捕捉了潜在的过程,但很难推断.
- 概率模型有助于推断,但可能不能完全代表生成机制.
研究的目的:
- 为将机械网络模型 (MNM) 转换为概率网络模型 (PNM) 制定一个一般框架.
- 为了使不同机械模型产生的网络属性的定量比较.
- 弥合机械和概率网络建模范式之间的差距.
主要方法:
- 为MNM转换为PNM引入一个新的框架.
- 识别基本的网络属性及其共同的概率分布.
- 应用框架来分析网络属性分布.
主要成果:
- 该框架允许识别关键的网络属性及其从MNM的概率分布.
- 允许直接比较不同机械模型的网络输出.
- 与参考模型对财产表示 (例如,聚类) 的评估.
结论:
- 拟议的框架成功地将机械和概率网络建模相结合.
- 通过概率表示来增强机械模型的分析能力.
- 突出了概率网络模型未来发展的领域.
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