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Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer

Sandipan Roy1, Yves Atchadé2, George Michailidis3

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Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|October 10, 2019
PubMed
Summary
This summary is machine-generated.

We developed a fast, scalable algorithm for analyzing social network data using stochastic blockmodels with node covariates. This method efficiently estimates model parameters, outperforming existing techniques on synthetic and real-world datasets.

Keywords:
Monte-Carlo EMcase-control approximationparallel computation with communicationsocial networksubsampling

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Area of Science:

  • Social network analysis
  • Statistical modeling
  • Machine learning

Background:

  • Social network analysis often employs stochastic blockmodels (SBMs) to identify community structures.
  • Incorporating node covariate information enhances SBMs for richer social network analysis.
  • Estimating parameters in these complex models can be computationally intensive.

Purpose of the Study:

  • To develop a computationally efficient and scalable algorithm for parameter estimation in SBMs with node covariates.
  • To enable accurate analysis of large-scale social network data.

Main Methods:

  • A novel Monte Carlo Expectation-Maximization (EM) algorithm is proposed.
  • The algorithm utilizes a case-control approximation of the log-likelihood.
  • A subsampling approach and parallel processing enhance scalability and speed.

Main Results:

  • The proposed algorithm demonstrates fast and scalable performance for SBM parameter estimation.
  • Evaluations on synthetic datasets show competitive or superior performance compared to existing methods.
  • The model is successfully applied to a Facebook social network dataset with node covariates.

Conclusions:

  • The developed algorithm provides an efficient solution for analyzing social networks with node covariates using SBMs.
  • Its parallelizability and scalability make it suitable for large-scale network data analysis.
  • This approach advances the field of statistical network analysis.