Fig 1.
Two basic mechanisms to understand the origin of homophily.
Nodes represent individuals, different colors indicate different traits. Links correspond to social ties, e.g. friendship. Increase of similarity between connected individuals may either arise from changes in traits (socialization process) (a), where individuals change their trait according to the dominance of that trait in their local social networks, or through re-wiring of their local social networks (social selection process) (b), where individuals re-shape their social contacts such that their trait matches those in the new environment better.
Fig 2.
Snapshot of the friendship network of university students.
The network is reconstructed from students’ interactions on the social network site VK, the Russian variant of Facebook. Nodes represent students, links exist if one student gave a “like” to another at least once in March 2016. Color represent the performance (GPA) of students across the whole period of studies. There is visible clustering of students with similar GPA.
Fig 3.
Homophily of students with good (a) and (b), and excellent grades (c) and (d), as a function of social distance.
Observed increase in probability IX that a student is in the top Xth percentile of students, given that their friends are also in the top Xth percentile. Results for the high school are shown in (a) and (c), for university in (b) and (d). Vertical lines indicate 95% confidence intervals computed with the permutation test. The social distance of 1 means friends, the social distance of 2 means friends of friends and the social distance of 3 means friends of friends of friends.
Fig 4.
Evolution of homophily (Homophily index) in friendship networks of high school (a) and university students (b).
Homophily increases with time by almost a factor of 2 (circles). The significance of the observed effect is measured with a randomization test (triangles), where grades were reshuffled randomly between the nodes in the network. It is amazing that when the GPAs of individual students are fixed to their temporal average (crosses), practically the same increase of homophily is observed, which signals the dominance of network restructuring. Results can be understood with a simple model (squares). Vertical bars are standard deviations.
Fig 5.
The network properties degree and clustering change over time (relative changes are shown, first time point is 1).
While the network of seniors becomes sparser, there is a densification of the high school network (inset). Therefore degree and clustering coefficients can not be the drivers behind the observed homophily increase in both groups.