Mixed Membership Stochastic Blockmodels
Published Version
http://www.jmlr.org/papers/v9/airoldi08a.htmlMetadata
Show full item recordCitation
Airoldi, Edoardo M., David M. Blei, Stephen E. Fienberg, and Eric P. Xing. "Mixed Membership Stochastic Blockmodels." Journal of Machine Learning Research 9 (June 1, 2008): 1981-2014.Abstract
Consider data consisting of pairwise measurements, such as presence or absence of links between pairs of objects. These data arise, for instance, in the analysis of protein interactions and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing pair- wise measurements with probabilistic models requires special assumptions, since the usual inde- pendence or exchangeability assumptions no longer hold. Here we introduce a class of variance allocation models for pairwise measurements: mixed membership stochastic blockmodels. These models combine global parameters that instantiate dense patches of connectivity (blockmodel) with local parameters that instantiate node-specific variability in the connections (mixed membership). We develop a general variational inference algorithm for fast approximate posterior inference. We demonstrate the advantages of mixed membership stochastic blockmodels with applications to so- cial networks and protein interaction networks.Terms of Use
This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAACitable link to this page
http://nrs.harvard.edu/urn-3:HUL.InstRepos:37304567
Collections
- FAS Scholarly Articles [18292]
Contact administrator regarding this item (to report mistakes or request changes)
Comments made during the workflow steps
cat.complete