Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques
<|reference_start|>Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques: 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.<|reference_end|>
@article{marqués-sánchez2024adolescent,
    title={Adolescent relational behaviour and the obesity pandemic: A descriptive
  study applying social network analysis and machine learning techniques},
    author={Pilar Marqu'es-S'anchez, Mar'ia Cristina Mart'inez-Fern'andez,
  Jos'e Alberto Ben'itez-Andrades, Enedina Quiroga-S'anchez, Mar'ia Teresa
  Garc'ia-Ord'as and Natalia Arias-Ramos},
    journal={Plos One, Volume 18, Issue 8, August 2023, e0289553},
    year={2024},
    doi={10.1371/journal.pone.0289553},
    archivePrefix={arXiv},
    eprint={2402.03385},
    primaryClass={cs.SI cs.LG}
}