Partition-Merge: Distributed Inference and Modularity Optimization
IEEE Access, 2021 This paper presents a novel meta-algorithm, Partition-Merge (PM), which takes existing centralized algorithms for graph computation and makes them distributed and faster. In a nutshell, PM divides the graph into small subgraphs using our novel randomized
Vincent Blondel +4 more
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DragDL:An Easy-to-Use Graphical DL Model Construction System [PDF]
Jisuanji kexue, 2021 Deep learning has broad applications in various fields.However,users still need to face problems from two aspects when applying deep learning.First,deep learning has a complex theoretical background,non-professional users lack background knowledge in ...
TANG Shi-zheng, ZHANG Yan-feng
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Incomplete graphical model inference via latent tree aggregation [PDF]
, 2018 Graphical network inference is used in many fields such as genomics or ecology to infer the conditional independence structure between variables, from measurements of gene expression or species abundances for instance.
Ambroise, Christophe +2 more
core +4 more sources
High dimensional semiparametric latent graphical model for mixed data [PDF]
, 2014 We propose a semiparametric latent Gaussian copula model for modelling mixed multivariate data, which contain a combination of both continuous and binary variables. The model assumes that the observed binary variables are obtained by dichotomizing latent
Jianqing Fan, Han Liu, Y. Ning, H. Zou
semanticscholar +1 more source
Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model [PDF]
Journal of the American Statistical Association, 2013 We propose an asymptotically normal and efficient procedure to estimate every finite subgraph for covariate-adjusted Gaussian graphical model. As a consequence, a confidence interval as well as p-value can be obtained for each edge.
Mengjie Chen +3 more
semanticscholar +1 more source
Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data
IEEE Access, 2020 The sparse Cholesky parametrization of the inverse covariance matrix is directly related to Gaussian Bayesian networks. Its counterpart, the covariance Cholesky factorization model, has a natural interpretation as a hidden variable model for ordered ...
Irene Cordoba +3 more
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Copula Gaussian graphical models and their application to modeling functional disability data [PDF]
Annals of Applied Statistics 2011, Vol. 5, No. 2A, 969-993, 2011 We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models (CGGMs) and embed graphical model selection inside a semiparametric Gaussian copula.
arxiv +1 more source
FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks
PLoS Comput. Biol., 2016 Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables.
Ting Wang +8 more
semanticscholar +1 more source
Learning Graphical Model Parameters with Approximate Marginal Inference [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013 Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model misspecification. This paper studies methods that fit parameters directly to maximize a measure of the accuracy of predicted marginals ...
Justin Domke
semanticscholar +1 more source
Probabilistic Community Using Link and Content for Social Networks
IEEE Access, 2017 Community detection is one of the most important problems in social network analysis in the context of the structure of underlying graphs. Many researchers have proposed methods, which only consider the network structure of social networks, for ...
Shuai Zhao, Le Yu, Bo Cheng
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