Modeling Diversity Dynamics in Time-Evolving Collaboration Networks

Christopher Archer

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2025-122
May 17, 2025

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-122.pdf

Increasing diversity in a community or an organization requires paying attention to many different aspects, including recruitment, hiring, retention, climate, and more. In this paper, we focus on how climate, captured through network interactions, can affect the growth or decay of minority populations within that community. Building on previous work, we develop a dynamic stochastic block model that grows according to a weighted version of preferential attachment, while having some memory of previous edges as well. This models how interactions between nodes in the network can influence the recruitment of new nodes to the network. We derive a deterministic approximation of this random system and prove its convergence is determined by the network parameters. Additionally, we show how the memory of the network affects convergence under different parameter regimes, and we validate this model by assessing the growth of women scientists in the American Physics Society's co-authorship network. We conclude with two extensions to the model, accounting for "soft" homophily in recruitment as well as node departures.

Advisor: Gireeja Ranade

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BibTeX citation:

@mastersthesis{Archer:EECS-2025-122,
    Author = {Archer, Christopher},
    Editor = {Ranade, Gireeja},
    Title = {Modeling Diversity Dynamics in Time-Evolving Collaboration Networks},
    School = {EECS Department, University of California, Berkeley},
    Year = {2025},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-122.html},
    Number = {UCB/EECS-2025-122},
    Abstract = {Increasing diversity in a community or an organization requires paying attention to many different aspects, including recruitment, hiring, retention, climate, and more. In this paper, we focus on how climate, captured through network interactions, can affect the growth or decay of minority populations within that community. Building on previous work, we develop a dynamic stochastic block model that grows according to a weighted version of preferential attachment, while having some memory of previous edges as well. This models how interactions between nodes in the network can influence the recruitment of new nodes to the network. We derive a deterministic approximation of this random system and prove its convergence is determined by the network parameters. Additionally, we show how the memory of the network affects convergence under different parameter regimes, and we validate this model by assessing the growth of women scientists in the American Physics Society's co-authorship network. We conclude with two extensions to the model, accounting for "soft" homophily in recruitment as well as node departures.}
}

EndNote citation:

%0 Thesis
%A Archer, Christopher
%E Ranade, Gireeja
%T Modeling Diversity Dynamics in Time-Evolving Collaboration Networks
%I EECS Department, University of California, Berkeley
%D 2025
%8 May 17
%@ UCB/EECS-2025-122
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2025/EECS-2025-122.html
%F Archer:EECS-2025-122