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Recovering Social Networks from Individual Attributes

Bibliographic Data

ID10142169
AuthorsArnold Polanski (0000-0001-9146-6364, Queen's University Belfast, corresponding author), Duncan Mcvicar (0000-0002-1356-3799, Queen's University Belfast)
Year2011
Volume35
Issue4
Pages287-311
Publication date2011-09-16
Open AccessNo
TypeARTICLE
VenueJournal of Mathematical Sociology (JOURNAL)
Journal identifiersISSN: 0022-250X • E-ISSN: 1545-5874
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/0022250x.2010.509525
OpenAlexW2068846572
LanguageEN
References cited37

One of the most important challenges of network analysis remains the scarcity of reliable information on existing connection structures. This work explores theoretical and empirical methods of inferring directed networks from nodes attributes and from functions of these attributes that are computed for connected nodes. We discuss the conditions, under which an underlying connection structure can be (probabilistically) recovered, and propose a Bayesian recovery algorithm. In an empirical application, we test the algorithm on the data from the European School Survey Project on Alcohol and Other Drugs

Adjacency matrix · Connection (principal bundle · Empirical research · Mathematical economics · Miller · Operations research · Sociology · Statistics · Test (biology · Bayesian Modeling and Causal Inference · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Theoretical Computer Science

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Highly citedNo

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