Deep description of static and dynamic network ties in Honduran villages

2026-02-17

AI Summary

This study investigates how individual attributes and community characteristics jointly shape the static structure and dynamic evolution of multidimensional social networks—including friendship, health advice, economic support, and conflict—in rural Honduras. Leveraging a longitudinal census of 20,232 residents across 176 villages in Copán Department in 2016 and 2019, the authors construct multilayer social networks and employ a three-tiered analytical framework encompassing individual, dyadic, and community levels. Using mixed-effects zero-inflated negative binomial models, dyadic homophily analyses, and community-level network metrics, the research offers the first integrated analysis of cooperative and conflictual networks in a rural development context. It reveals persistent homophily effects by gender and religion across relationship types and demonstrates that the influence of education and economic factors on social ties has significantly intensified over time, thereby advancing understanding of the dynamic mechanisms underlying rural social structures.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Social networks and social learning

Abstract

We examine static and dynamic social network structure in 176 villages within the Copan Department of Honduras across two data waves (2016, 2019), using detailed data on multiplex networks for 20,232 individuals enrolled in a longitudinal survey. These networks capture friendship, health advice, financial help, and adversarial relationships, allowing us to show how cooperation and conflict jointly shape social structure. Using node-level network measures derived from near-census sociocentric village networks, we leverage mixed-effects zero-inflated negative binomial models to assess the influence of individual attributes, such as gender, marital status, education, religion, and indigenous status, and of village characteristics, on the dynamics of social networks over time. We complement these node-level models with dyadic assortativity (odds-ratio-based homophily) and community-level measures to describe how sorting by key attributes differs across network types and between waves. Our results demonstrate significant assortativity based on gender and religion, particularly within health and financial networks. Across networks, gender and religion exhibit the most consistent assortative mixing. Additionally, community-level assortativity metrics indicate that educational and financial factors increasingly influence social ties over time. Our findings provide insights into how personal attributes and community dynamics interact to shape network formation and socio-economic relationships in rural settings over time.

Problem

• social network dynamics
• assortativity
• rural communities
• multiplex networks
• longitudinal survey

Innovation

• multiplex networks
• dyadic assortativity
• zero-inflated negative binomial models
• sociocentric network
• longitudinal social network analysis