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MAXIMUM LIKELIHOOD ESTIMATION FOR SOCIAL NETWORK DYNAMICS.

Tom A B Snijders1, Johan Koskinen2, Michael Schweinberger3

  • 1University of Oxford ; University of Groningen.

The Annals of Applied Statistics
|November 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for analyzing network panel data, interpreting network changes as actor choices. The Maximum Likelihood estimator is more efficient for small datasets than the Method of Moments estimator.

Keywords:
GraphsLongitudinal dataMethod of momentsRobbins-Monro algorithmStochastic approximation

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Area of Science:

  • Social network analysis
  • Statistical modeling
  • Network dynamics

Background:

  • Network panel data requires models that capture evolving relationships.
  • Existing methods may lack efficiency for smaller datasets.

Purpose of the Study:

  • To develop a parametric model for network panel data.
  • To provide an efficient estimation algorithm for network dynamics.
  • To compare Maximum Likelihood with Method of Moments estimators.

Main Methods:

  • Modeling network data as a continuous-time Markov process on directed graphs.
  • Utilizing data augmentation and stochastic approximation for Maximum Likelihood estimation.
  • Applying the model to an evolving friendship network and conducting simulations.

Main Results:

  • A novel parametric model for network panel data is presented.
  • An algorithm for Maximum Likelihood estimation is developed.
  • The Maximum Likelihood estimator demonstrates higher efficiency than the Method of Moments estimator for small datasets.

Conclusions:

  • The proposed model offers a robust framework for social network analysis.
  • The Maximum Likelihood estimator provides a more efficient approach for analyzing evolving networks, particularly with limited data.