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Modeling Diversity Dynamics in Time-Evolving Collaboration Networks

Authors Christopher Archer , Gireeja Ranade

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Author Details

Christopher Archer
  • University of California, Berkeley, CA, USA
Gireeja Ranade
  • University of California, Berkeley, CA, USA

Funding

Thanks to the NSF for CAREER grant ECCS-2240031.

Acknowledgements

Additionally, the authors would like to thank user "Actually Fritz" From Mathematics StackExchange for assisting in the proof of Lemma 6. We also thank the reviewers whose comments improved the presentation of this paper. Thanks to The American Physics Society (APS) dataset for the use of their dataset which can be requested at https://journals.aps.org/datasets.

Cite AsGet BibTex

Christopher Archer and Gireeja Ranade. Modeling Diversity Dynamics in Time-Evolving Collaboration Networks. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 6:1-6:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.FORC.2024.6

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Sociology
Keywords
  • Network Models
  • Diversity
  • Collaboration Networks
  • Stochastic Block Model

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