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复杂系统与复杂性科学  2014, Vol. 11 Issue (1): 87-94    DOI: 10.13306/j.1672-3813.2014.01.011
  本期目录 | 过刊浏览 | 高级检索 |
基于复杂网络理论的肥胖症影响因素研究
许小可1, 张海峰2, 方锦清3
1.大连民族学院信息与通信工程学院,辽宁 大连 116600;
2.安徽大学数学与科学学院,合肥 230039;
3.中国原子能科学研究院,北京 102413
Study of Obesity Influence Factors Based on Complex Network Theory
XU Xiao-ke1, ZHANG Hai-feng2, FANG Jin-qing3
1. College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116600, China;
2. School of Mathematical Science, Anhui University, Hefei 230039, China;
3. China Institute of Atomic Energy, Beijing 102413, China
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摘要 常规研究方法一般是在线形模型的基础探讨遗传、膳食和身体活动等多种因素对肥胖症的影响,很难对肥胖症影响因素做出全面的、系统性的描述和分析,无法有效预防和控制肥胖。以英国权威科学家提出的影响肥胖的八大类别109个变量为基础,在复杂网络研究框架下定性和定量分析了所有变量之间的相互影响及其拓扑结构特征,探讨了影响成人超重肥胖的重要因素,并研究了多尺度下肥胖症各因素之间的关联性。
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许小可
张海峰
方锦清
关键词 复杂网络肥胖症复杂系统节点重要性    
Abstract:The conventional research method of obesity is generally on the basis of linear models, and only some important factors like genetic, dietary and physical activity have been discussed. Actually, the above linear method is difficult not only for making a comprehensive systematic description and analysis for obesity influence factors, but also for effectively preventing and controlling the adult obesity. In this study, we used the 109 variables in eight categories proposed by the British authority scientists to study obesity, and we qualitatively and quantitatively analyzed the interaction between all the variables and their network topological structure based on complex network theory. Furthermore, we explored important factors of influencing adult overweight and obesity, and we studied the multi-scale correlation among various factors.
Key wordscomplex networks    obesity    complex system    node importance
收稿日期: 2013-08-30      出版日期: 2026-06-22
基金资助:国家自然科学基金(61004104,61104143,61174151);中央高校基本科研业务费专项基金(DC13010215)
作者简介: 许小可(1979-),男,辽宁大连人,博士,教授,主要研究方向为非线性时间序列分析和复杂网络。
引用本文:   
许小可, 张海峰, 方锦清. 基于复杂网络理论的肥胖症影响因素研究[J]. 复杂系统与复杂性科学, 2014, 11(1): 87-94.
XU Xiao-ke, ZHANG Hai-feng, FANG Jin-qing. Study of Obesity Influence Factors Based on Complex Network Theory[J]. Complex Systems and Complexity Science, 2014, 11(1): 87-94.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2014.01.011      或      https://fzkx.qdu.edu.cn/CN/Y2014/V11/I1/87
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