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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Document hierarchies from text and links
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Asymptotic mutual information for the binary stochastic block model
TL;DR: An information-theoretic view of the stochastic block model, a popular statistical model for the large-scale structure of complex networks, is developed, which establishes an explicit `single-letter' characterization of the per-vertex mutual information between the vertex labels and the graph, when the graph average degree diverges.
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Overlapping stochastic block models with application to the French political blogosphere
TL;DR: The Overlapping Stochastic Block Model (OSMBM) as mentioned in this paper allows the vertices to belong to multiple clusters, and, to some extent, generalizes the well-known stochastic block model [Nowicki and Snijders (2001].
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Community detection in multi-relational data with restricted multi-layer stochastic blockmodel
Subhadeep Paul,Yuguo Chen +1 more
TL;DR: In this paper, the authors consider two random graph models, the multi-layer stochastic block model (MLSBM) and a model with a restricted parameter space, and derive consistency results for community assignments of the maximum likelihood estimators (MLEs) in both models.
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Detecting overlapping protein complexes based on a generative model with functional and topological properties
TL;DR: A Generative Model with Functional and Topological Properties (GMFTP) is developed to describe the generative processes of the PPI network and the functional profile and shows that GMFTP has a competitive performance over the state-of-the-art approaches.
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