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Preserving Multiple Homophilies in a Network Configuration Model.

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    Respondent-driven sampling (RDS) models hidden populations using social networks. This study presents a new method to scale RDS data, preserving network homophily for better population estimates.

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    Area of Science:

    • Social Network Analysis
    • Statistical Modeling
    • Epidemiology

    Background:

    • Respondent-driven sampling (RDS) is widely used for surveying hidden populations via social networks.
    • The underlying social network structure and its properties, like homophily, are often unknown.
    • Estimating population size and feature proportions from RDS data is challenging.

    Purpose of the Study:

    • To develop a methodology for scaling Respondent-driven sampling (RDS) data to model hidden populations.
    • To preserve multiple homophilies (the tendency of similar individuals to associate) among different features within the modeled population.
    • To create a realistic network model from RDS data for further analysis and simulation.

    Main Methods:

    • Developed a network generation methodology to scale RDS data.
    • Preserved multiple homophilies across 46 features using the SATHCAP RDS survey data.
    • Validated the methodology by generating a Barabasi-Albert network and simulating RDS surveys on the modeled network.

    Main Results:

    • The network generation methodology successfully preserved homophilic associations in a generated Barabasi-Albert network with under 2% error for 85% of homophilies.
    • Simulated RDS surveys on the generated network preserved 85% of homophilies with under 15% error.
    • The study successfully created a realistic model of an expanded social network from RDS data.

    Conclusions:

    • The proposed methodology effectively scales Respondent-driven sampling (RDS) data to model hidden populations while preserving key network properties like homophily.
    • This approach enables more accurate estimation of population size and feature proportions from RDS surveys.
    • The generated network models provide a valuable tool for understanding social network dynamics and simulating survey outcomes.