Recovering social networks from individual attributes

Polanski, Arnold and McVicar, Duncan (2011) Recovering social networks from individual attributes. Journal of Mathematical Sociology, 35 (4). pp. 287-311. ISSN 0022-250X

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Abstract

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.

Item Type:	Article
Faculty \ School:	Faculty of Social Sciences > School of Economics
UEA Research Groups:	Faculty of Social Sciences > Research Groups > Economic Theory Faculty of Social Sciences > Research Groups > Applied Econometrics And Finance Faculty of Science > Research Groups > Statistics
Depositing User:	Julie Frith
Date Deposited:	09 Feb 2012 10:31
Last Modified:	28 Mar 2025 02:53
URI:	https://ueaeprints.uea.ac.uk/id/eprint/36952
DOI:	10.1080/0022250X.2010.509525

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