Engineering Social Networks: How Initial Group Assignment Shapes Student Social Interactions

2026-07-20

AI Summary

This study addresses a critical limitation in traditional social network research, which often overlooks triadic closure mechanisms by modeling only dyadic relationships, thereby misestimating the effects of randomized grouping policies. Leveraging random assignment of students to classrooms and small groups, the authors integrate multi-source behavioral data—including phone calls, text messages, physical co-presence, and social media interactions—to develop a subgraph generation model that explicitly incorporates randomized interventions with triadic closure dynamics. The findings reveal that group assignment primarily shapes network structure by facilitating triadic closure rather than direct ties; neglecting this mechanism leads to severe underestimation of its impact. Notably, in social groups, nearly all observed network effects are driven by induced triadic closure, offering a precise basis for evaluating educational grouping strategies.

Abstract

A large literature uses exogenous variation to estimate how assignment to classrooms or other groups shapes social networks. Yet most of these analyses remain dyadic, treating each link in isolation, even though ties often form through triadic closure, as a friend of a friend also becomes a friend. Using fine-grained data on phone calls, text messages, physical co-location, and social-media ties, we estimate the network formation effects of randomly assigning first-year university students to classrooms and to smaller social groups. To analyze explicitly whether group assignment interact with triadic closure, we use our random assignment to estimate a subgraph generated model of network formation. Accounting for triadic closure turns out to be crucial. For social groups in particular, group assignment affects network formation almost entirely by inducing additional triadic closure. Estimates ignoring triadic closure can thus yield misleading predictions about the network effects and benefits of group assignment policies.

Problem

• social networks
• group assignment
• triadic closure
• network formation
• dyadic analysis

Innovation

• triadic closure
• random assignment
• network formation
• subgraph generation
• social networks