Emergence of Hierarchy in Networked Endorsement Dynamics

TL;DR

This paper introduces a generative model for the emergence and persistence of hierarchies in social and biological networks, explaining how different structures form and endure over time.

Contribution

It presents a new dynamic network model that captures hierarchy formation, supports statistical inference, and compares mechanisms across diverse empirical data.

Findings

Model produces a spectrum of hierarchical structures.

Identifies critical points separating different regimes.

Applied to real data revealing distinct generative mechanisms.

Abstract

Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks.