Published September 2009
| Published
Journal Article
Open
Identifying Community Structures from Network Data via Maximum Likelihood Methods
- Creators
- Čopič, Jernej
-
Jackson, Matthew O.
- Kirman, Alan
Chicago
An error occurred while generating the citation.
Abstract
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.
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.Attached Files
Published - bejte.2009.9.1.1523.pdf
Files
bejte.2009.9.1.1523.pdf
Files
(1.3 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:4e1ed4f323b36464e843dc6fe20b1135
|
1.3 MB | Preview Download |
Additional details
- Eprint ID
- 59576
- Resolver ID
- CaltechAUTHORS:20150817-100023476
- NSF
- SES-0316493
- NSF
- SES-0647867
- Guggenheim Foundation
- Center for Advanced Studies in the Behavioral Sciences
- Caltech Lee Center for Advanced Networking
- Caltech Social and Information Sciences Laboratory
- Created
-
2015-08-17Created from EPrint's datestamp field
- Updated
-
2021-11-10Created from EPrint's last_modified field