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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Computational Statistical Methods for Social Network Models.

David R Hunter1, Pavel N Krivitsky, Michael Schweinberger

  • 1Department of Statistics, Pennsylvania State University, University Park, PA ( dhunter@stat.psu.edu ).

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|July 6, 2013
PubMed
Summary
This summary is machine-generated.

This review covers statistical social network models, focusing on computational methods like exponential-family random graph models (ERGM) and latent variable models. It highlights techniques for complete and incomplete networks, aiding reproducible research.

Keywords:
DegeneracyERGMLatent variablesMCMC MLEVariational methods

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Area of Science:

  • Social network analysis
  • Statistical modeling
  • Computational statistics

Background:

  • Social network analysis encompasses diverse statistical methodologies.
  • Recent advancements emphasize computational aspects for analyzing network data.
  • Understanding network structures requires robust statistical frameworks.

Purpose of the Study:

  • To provide a comprehensive review of recent statistical work in social network models.
  • To emphasize the computational aspects of these statistical methods.
  • To cover models for complete and incomplete networks, observed at single or multiple time points.

Main Methods:

  • Review of exponential-family random graph models (ERGM).
  • Review of latent variable models for network data.
  • Discussion of methods for incompletely observed and longitudinal networks.

Main Results:

  • Numerous modeling techniques are identified and cited.
  • Illustrative examples are provided using Sampson's monks dataset.
  • Code is supplied for analysis duplication.

Conclusions:

  • The review offers a broad overview of statistical social network models.
  • Emphasis is placed on computational feasibility and reproducibility.
  • The cited literature and provided examples facilitate further research.