青少年人际关系行为与肥胖流行：一项应用社会网络分析和机器学习技术的描述性研究
Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques
AI总结：
本研究运用社会网络分析和无监督机器学习技术，分析青少年班级内基于饮食和性别属性的亚群体形成，发现群体凝聚力与肥胖相关因素相似性有关，并验证了同质性概念。
摘要：
目的：通过探索群体节点属性之间在饮食和性别方面的相似性，研究亚群体的存在性，并基于群体间的相似性方面，通过社会网络分析（SNA）和人工智能技术分析群体之间的连通性。方法：2015年3月至12月期间，来自5个不同教育中心的235名学生参与了本研究。所进行的数据分析分为两个部分：社会网络分析和无监督机器学习技术。在社会网络分析方面，应用了Girvan-Newman技术来寻找所分析的不同班级的友谊网络内最合适的凝聚群体数量。结果：在三个班级中应用Girvan-Newman后，最佳聚类划分分别为：A班2个聚类，B班7个聚类，C班6个聚类。群体与性别和饮食变量之间存在显著差异。在将人口饮食作为输入变量应用K-means后，得到A班2个聚类、B班3个聚类和C班3个聚类的K-means聚类结果。结论：青少年在班级内形成亚群体。亚群体的凝聚力由节点在影响肥胖的方面共享相似性这一事实所定义，即它们共享与食物质量和性别相关的属性。与社会网络分析相关的同质性概念证明了我们的结果。人工智能技术结合Girvan-Newman的应用为亚群体之间相似性和凝聚力的结构分析提供了稳健性。
英文摘要：
Aim: To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. Methods: 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. Results: After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. Conclusion: Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
