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Identifying Community Structures from Network Data via Maximum Likelihood Methods

Čopič, Jernej and Jackson, Matthew O. and Kirman, Alan (2009) Identifying Community Structures from Network Data via Maximum Likelihood Methods. B.E. Journal of Theoretical Economics, 9 (1). Art. No. 30. ISSN 1935-1704.

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Networks of social and economic interactions are often influenced by unobserved structures among the nodes. Based on a simple model of how an unobserved community structure generates networks of interactions, we axiomatize a method of detecting the latent community structures from network data. The method is based on maximum likelihood estimation.

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Jackson, Matthew O.0000-0001-9846-4249
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Additional Information:© 2009 B.E. Press. We gratefully acknowledge financial support under NSF grant SES-0316493 and SES-0647867, as well as from the Guggenheim Foundation, the Center for Advanced Studies in the Behavioral Sciences, the Lee Center for Advanced Networking, the Institute for Advanced Study and the Social Information Sciences Laboratory at Caltech. We thank Bhaskar Dutta, Matteo Marsili, and participants of the ISS seminar at Cornell for helpful discussions, Pritha Dev for pointing out an error in a previous version, and Rik Pieters and Hans Baumgartner for making their data available.
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Guggenheim FoundationUNSPECIFIED
Center for Advanced Studies in the Behavioral SciencesUNSPECIFIED
Caltech Lee Center for Advanced NetworkingUNSPECIFIED
Caltech Social and Information Sciences LaboratoryUNSPECIFIED
Subject Keywords:networks; communities; community structures; maximum likelihood; social networks
Issue or Number:1
Record Number:CaltechAUTHORS:20150817-100023476
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Official Citation:Jernej Copic, Matthew O. Jackson, and Alan Kirman (2009) “Identifying Community Structures from Network Data via Maximum Likelihood Methods,” The B.E. Journal of Theoretical Economics: Vol. 9: Iss. 1 (Contributions), Article 30.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:59576
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Deposited On:17 Aug 2015 17:11
Last Modified:03 Oct 2019 08:48

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