Adolescent relational behaviour and the obesity pandemic: A descriptive
  study applying social network analysis and machine learning techniques
Pilar Marqués-Sánchez María Cristina Martínez-Fernández José Alberto Benítez-Andrades Enedina Quiroga-Sánchez María Teresa García-Ordás Natalia Arias-Ramos
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.