Mixed Membership Stochastic Blockmodels
TLDR
In this article, the authors introduce a class of variance allocation models for pairwise measurements, called mixed membership stochastic blockmodels, which combine global parameters that instantiate dense patches of connectivity (blockmodel) with local parameters (mixed membership), and develop a general variational inference algorithm for fast approximate posterior inference.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 pairwise measurements with probabilistic models requires special assumptions, since the usual independence 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 social networks and protein interaction networks.read more
Citations
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Proceedings ArticleDOI
Detecting cohesive and 2-mode communities indirected and undirected networks
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Community Detection in Networks: The Leader-Follower Algorithm
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Mixed Membership Matrix Factorization
TL;DR: In this article, a fully Bayesian framework for integrating discrete mixed membership and continuous latent factor models into unified Mixed Membership Matrix Factorization (M3F) models is developed, and two M3F models, derived Gibbs sampling inference procedures, are introduced and validated on the EachMovie, MovieLens, and Netflix Prize collaborative filtering datasets.
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Fast Detection of Overlapping Communities via Online Tensor Methods
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Discrete Temporal Models of Social Networks
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