  Several studies have investigated human interaction using modern tracking
techniques for face-to-face encounters across various settings and age groups.
However, little attention has been given to understanding how individual
characteristics relate to social behavior. This is particularly important in
younger age groups due to its potential effects on early childhood development.
In this study, conducted during the Complexity 72h Workshop, we analyze human
social interactions in a French preschool, where children's face-to-face
interactions were monitored using proximity sensors over an academic year. We
use metadata from parent surveys and preschool linguistic tests, covering
demographic information and home habits, to examine the interplay between
individual characteristics and contact patterns. Using a mixture of approaches,
from random forest classifiers to network-based metrics at both dyadic and
higher-order (group) levels, we identify sex, age, language scores, and number
of siblings as the variables displaying the most significant associations with
interaction patterns. We explore these variables' relationships to interactions
within and outside classrooms and across mixed and single-grade classes. At the
group level, we investigate how group affinity affects group persistence. We
also find that higher-order network centrality (hypercoreness) is higher among
children with siblings, indicating different group embedding despite similar
total contact duration. This study aligns with existing literature on early
social development and highlights the importance of integrating individual
traits into the study of human interactions. Focusing on 2-5-year-olds offers
insights into emerging social preferences during critical phases of cognitive
development. Future research could use these findings to enhance mechanistic
models of complex social systems by incorporating individual traits.
