The origins of large-scale structure in family networks

TL;DR

This study analyzes a comprehensive family network dataset to understand how individual behaviors influence large-scale network structures, revealing that partner change dynamics, rather than partner-choice homophily, drive network growth and complexity.

Contribution

It demonstrates that partner-change behavior and self-excitation are key factors shaping large-scale family network structures, challenging previous emphasis on homophily.

Findings

Partner-change behavior creates network shortcuts.

Partner change probability increases with prior partners.

Homophily has little effect on large-scale structure.

Abstract

Family relations are the most fundamental of all social networks and encompass everyone. Family networks grow as individuals have children, creating connections between families, which over time create large and complex structures. While partner-choice homophily has been proposed as a key driver in this growth process, little is known about the connection between individual behavior and the emergent large-scale structure of family networks. Here, we analyze a unique population-complete family network, covering millions of individuals across several decades, enriched with demographic, educational, and geographic data from high-quality national registries. Drawing on the longitudinal coverage of our observations and using a series of growing-network models, we unravel how individual-level behavior shapes the large-scale network structure. Contrary to prevailing theories, we find that partner-choice homophily has little effect on the emergent large-scale structure. Instead, we identify two key drivers: First, partner-change behavior, where individuals leave one partner for another, creates `shortcuts' in the network akin to rewirings in the Watts-Strogatz model. These shortcuts decrease pathlengths and accelerate the emergence of meso-scale connected components. Second, we find that partner change is a self-exciting behavior, such that the probability of changing partner increases with an individual's prior number of partners. The self-exciting behavior accelerates the generation of large network components, with highly connected individuals functioning as network hubs. Accounting for this partner-change behavior, we are able to accurately capture multiple large-scale network properties of the empirical family network. Finally we show that homophily-driven behavior is not able to generate the observed network structure.