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
More filters
Proceedings Article
Modeling citation networks using Latent random offsets
TL;DR: A novel model is presented that integrates the merits of content and citation analyses into a single probabilistic framework and can be used to effectively explore a citation network and provide meaningful explanations for links while still maintaining competitive citation prediction performance.
Posted Content
GRAM: Scalable Generative Models for Graphs with Graph Attention Mechanism.
TL;DR: This paper proposes GRAM, a generative model for graphs that is scalable in all three contexts, especially in training, and aims to achieve scalability by employing a novel graph attention mechanism, formulating the likelihood of graphs in a simple and general manner.
Proceedings ArticleDOI
On the Formation of Circles in Co-authorship Networks
TL;DR: An unsupervised approach to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers using a variety of node features and node similarity measures is proposed.
Proceedings ArticleDOI
Learning, Analyzing and Predicting Object Roles on Dynamic Networks
TL;DR: A novel approach is proposed that identifies the role of each object, tracks the changes of object roles over time, and predicts the evolving patterns of the object roles in dynamic networks.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Journal ArticleDOI
Finding scientific topics
TL;DR: A generative model for documents is described, introduced by Blei, Ng, and Jordan, and a Markov chain Monte Carlo algorithm is presented for inference in this model, which is used to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics.
Journal ArticleDOI
Functional organization of the yeast proteome by systematic analysis of protein complexes
Anne-Claude Gavin,Markus Bösche,Roland Krause,Paola Grandi,Martina Marzioch,Andreas Bauer,Jörg Schultz,Jens Rick,Anne-Marie Michon,Cristina-Maria Cruciat,Marita Remor,Christian Höfert,Malgorzata Schelder,Miro Brajenovic,Heinz Ruffner,Alejandro Merino,Karin Klein,Manuela Hudak,David Dickson,Tatjana Rudi,Volker Gnau,Angela Bauch,Sonja Bastuck,Bettina Huhse,Christina Leutwein,Marie-Anne Heurtier,Richard R. Copley,Angela Edelmann,Erich Querfurth,Vladimir Rybin,Gerard Drewes,Manfred Raida,Tewis Bouwmeester,Peer Bork,Bertrand Séraphin,Bernhard Kuster,Gitte Neubauer,Giulio Superti-Furga +37 more
TL;DR: The analysis provides an outline of the eukaryotic proteome as a network of protein complexes at a level of organization beyond binary interactions, which contains fundamental biological information and offers the context for a more reasoned and informed approach to drug discovery.