Mixed membership stochastic blockmodels

Details Mixed membership stochastic blockmodels Mixed membership stochastic blockmodels

2007-05-30

arxiv.org/abs/0705.4485v1

Journal of Machine Learning Research, 9, 1981-2014.

Statistics

Methodology

46 pages, 14 figures, 3 tables

Scientific paper

Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assumptions underlying many boilerplate models no longer hold. In this paper, we describe a latent variable model of such data called the mixed membership stochastic blockmodel. This model extends blockmodels for relational data to ones which capture mixed membership latent relational structure, thus providing an object-specific low-dimensional representation. We develop a general variational inference algorithm for fast approximate posterior inference. We explore applications to social and protein interaction networks.

Affiliated with

Also associated with

No associations

Rating

Mixed membership stochastic blockmodels does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Mixed membership stochastic blockmodels, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Mixed membership stochastic blockmodels will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-288276