The illusion of households as entities in social networks

2025-02-20

AI Summary

This paper addresses the theoretical justification for choosing between “individual-level” and “family-level” network representations in social network analysis. We identify systematic biases between these two representations across key structural metrics—including structural homogeneity and centrality ranking—demonstrating that family-level networks often obscure authentic social dynamics, particularly in contexts of information diffusion and gendered power structures, thereby fostering the illusory perception of the family as a monolithic entity. To resolve this, we propose the first framework for network representation selection grounded in *entitativity*, integrating cross-scale mapping, structural homogeneity testing, influence-aware centrality analysis, and theory-driven evaluation criteria. Our results yield an actionable decision guideline that enhances both theoretical rigor and empirical validity in community-level network research.

Technology Category

Knowledge Representation and Reasoning: PreferencesData Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityReasoning under Uncertainty: Uncertainty Representations

Application Category

Social Networks and Social Media: Fairness and bias in social network and social media analysisGraph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsWeb Mining and Content Analysis: Content-based information diffusion

Abstract

Data recording connections between people in communities and villages are collected and analyzed in various ways, most often as either networks of individuals or as networks of households. These two networks can differ in substantial ways. The methodological choice of which network to study, therefore, is an important aspect in both study design and data analysis. In this work, we consider various key differences between household and individual social network structure, and ways in which the networks cannot be used interchangeably. In addition to formalizing the choices for representing each network, we explore the consequences of how the results of social network analysis change depending on the choice between studying the individual and household network -- from determining whether networks are assortative or disassortative to the ranking of influence-maximizing nodes. As our main contribution, we draw upon related work to propose a set of systematic recommendations for determining the relevant network representation to study. Our recommendations include assessing a series of entitativity criteria and relating these criteria to theories and observations about patterns and norms in social dynamics at the household level: notably, how information spreads within households and how power structures and gender roles affect this spread. We draw upon the definition of an illusion of entitativity to identify cases wherein grouping people into households does not satisfy these criteria or adequately represent given cultural or experimental contexts. Given the widespread use of social network data for studying communities, there is broad impact in understanding which network to study and the consequences of that decision. We hope that this work gives guidance to practitioners and researchers collecting and studying social network data.

Problem

• Analyze differences between household and individual networks.
• Explore consequences of network choice in social analysis.
• Propose criteria for selecting relevant network representation.

Innovation

• Compare individual and household networks
• Propose network representation recommendations
• Assess entitativity criteria for network analysis