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Modeling Partitions of Individuals

Bibliographic Data

ID6169528
AuthorsMarion Hoffman (0000-0002-0741-7760, Institute for Advanced Study in Toulouse, University of Toulouse 1 Capitole, Toulouse, France, corresponding author), Per Block (0000-0002-7583-2392, University of Zurich), Tom Ab Snijders (0000-0003-3157-4157, University of Groningen)
Year2023
Volume53
Issue1
Pages1-41
Publication date2023-01-30
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSociological Methodology (JOURNAL)
Journal identifiersISSN: 0081-1750 • E-ISSN: 1467-9531
PublisherSAGE Publishing (PUBLISHER • US)
DOI10.1177/00811750221145166
OpenAlexW3089639421
LanguageEN
Citations received6
References cited85

Despite the central role of self-assembled groups in animal and human societies, statistical tools to explain their composition are limited. The authors introduce a statistical framework for cross-sectional observations of groups with exclusive membership to illuminate the social and organizational mechanisms that bring people together. Drawing from stochastic models for networks and partitions, the proposed framework introduces an exponential family of distributions for partitions. The authors derive its main mathematical properties and suggest strategies to specify and estimate such models. A case study on hackathon events applies the developed framework to the study of mechanisms underlying the formation of self-assembled project teams

Data science · Econometrics · Exponential random graph models · Management science · Random graph · Statistical model · Artificial Intelligence · Biomedical and Engineering Education · Computer Science · Engineering · Evolution and Genetic Dynamics · Mathematics · Theoretical Computer Science

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Unique citing works6
Citations per year2
Citation span2023 - 2025 (3)
Citation velocityrecent
Highly citedNo
Citation typesNeutral: 6

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