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Fitting ERGMs on big networks.

Weihua An1

  • 1Departments of Statistics and Sociology, Indiana University Bloomington, 752 Ballantine Hall, 1020 East Kirkwood Avenue, Bloomington, IN 47405, USA.

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|August 3, 2016
PubMed
Summary
This summary is machine-generated.

This study presents methods for fitting exponential random graph models (ERGMs) on large social networks. It addresses computational and conceptual challenges, offering a framework for scalable network analysis.

Keywords:
Big networksERGMsLink tracingMCMLEMeta network analysisPMLE

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Area of Science:

  • Social network analysis
  • Statistical modeling
  • Computational social science

Background:

  • Exponential Random Graph Models (ERGMs) are widely used for social network analysis.
  • ERGMs offer flexibility in modeling tie formation with covariates and endogenous processes.
  • Fitting ERGMs on large networks presents significant conceptual and computational challenges.

Purpose of the Study:

  • To describe a framework and methods for fitting ERGMs on big networks.
  • To address the conceptual and computational issues associated with large-scale ERGM analysis.
  • To provide guidance on the applicability of different methods for scalable network modeling.

Main Methods:

  • Leveraging existing algorithms to develop a scalable framework for ERGM fitting.
  • Describing a series of methods to overcome computational and conceptual hurdles.
  • Illustrating selected methods with practical examples.

Main Results:

  • A framework and methods are presented to facilitate ERGM fitting on large networks.
  • The advantages, disadvantages, and applicability conditions of various methods are outlined.
  • Demonstration of selected methods through illustrative examples.

Conclusions:

  • The proposed framework and methods offer solutions for analyzing large social networks using ERGMs.
  • Understanding the trade-offs of different methods is crucial for effective application.
  • This work contributes to the advancement of scalable network analysis techniques.