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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Book ChapterDOI
Consistent Estimation of Mixed Memberships with Successive Projections
TL;DR: The new algorithm successive projection overlapping clustering (SPOC) is presented which combines the ideas of spectral clustering and geometric approach for separable non-negative matrix factorization and is provably consistent under MMSB.
Node Clustering in Graphs: An Empirical Study
TL;DR: This paper presents an empirical study that compares the node clustering performances of state-of-the-art algorithms from both the probabilistic and spectral families on undirected graphs and shows that no family dominates over the other and that network characteristics play a significant role in determining the best model to use.
Posted Content
Two-sample Test of Community Memberships of Weighted Stochastic Block Models.
Yezheng Li,Hongzhe Li +1 more
TL;DR: A test statistic based on singular subspace distance is developed and under the weighted stochastic block models with dense graphs, the limiting distribution of the proposed test statistic is developed.
Posted Content
Variational Inference for Stochastic Block Models from Sampled Data
TL;DR: This article deals with nonobserved dyads during the sampling of a network and consecutive issues in the inference of the stochastic block model (SBM), and introduces variants of the variational EM algorithm for inferring the SBM under various sampling designs.
Book ChapterDOI
Community Detection on Weighted Networks: A Variational Bayesian Method
TL;DR: This paper extends the constrained Stochastic Block Model (conSBM) on weighted networks and uses a Bayesian method for both parameter estimation and community number identification, and develops a variational Bayesian Method for inference and parameter estimation.
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