Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication
We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-...
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| Vydané v: | Journal of computational and graphical statistics Ročník 28; číslo 3; s. 609 - 619 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Taylor & Francis
03.07.2019
American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundation of North America Taylor & Francis Ltd |
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| ISSN: | 1061-8600, 1537-2715 |
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| Abstract | We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic datasets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. Supplemental materials for this article are available online. |
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| AbstractList | We consider a stochastic blockmodel equipped with node covariate information, that is, helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic datasets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. Supplemental materials for this article are available online. We consider a stochastic blockmodel equipped with node covariate information, that is helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic data sets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. We consider a stochastic blockmodel equipped with node covariate information, that is helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic data sets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information.We consider a stochastic blockmodel equipped with node covariate information, that is helpful in analyzing social network data. The key objective is to obtain maximum likelihood estimates of the model parameters. For this task, we devise a fast, scalable Monte Carlo EM type algorithm based on case-control approximation of the log-likelihood coupled with a subsampling approach. A key feature of the proposed algorithm is its parallelizability, by processing portions of the data on several cores, while leveraging communication of key statistics across the cores during each iteration of the algorithm. The performance of the algorithm is evaluated on synthetic data sets and compared with competing methods for blockmodel parameter estimation. We also illustrate the model on data from a Facebook derived social network enhanced with node covariate information. |
| Author | Atchadé, Yves Roy, Sandipan Michailidis, George |
| Author_xml | – sequence: 1 givenname: Sandipan surname: Roy fullname: Roy, Sandipan email: sandipan@umich.edu organization: Department of Statistical Science, University College London – sequence: 2 givenname: Yves surname: Atchadé fullname: Atchadé, Yves organization: Department of Statistics, University of Michigan – sequence: 3 givenname: George surname: Michailidis fullname: Michailidis, George organization: Department of Statistics & Informatics Institute, University of Florida |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31595140$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Case-control approximation Computer simulation Iterative methods Mathematical models Maximum likelihood estimates Monte Carlo EM Multivariate, Multilevel, and Mixture Modelling Parallel computation with communication Parallel processing Parameter estimation Social network Social networks Subsampling |
| Title | Likelihood Inference for Large Scale Stochastic Blockmodels With Covariates Based on a Divide-and-Conquer Parallelizable Algorithm With Communication |
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