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Adaptive importance sampling for network growth models.

Adam N Guetz1, Susan P Holmes1

  • 1Stanford University, Stanford, CA, USA.

Annals of Operations Research
|May 17, 2016
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Summary
This summary is machine-generated.

This study introduces a new Adaptive Importance Sampling algorithm for estimating network growth model likelihoods. This method improves statistical inference for complex network analysis, offering more reliable model selection.

Keywords:
Adaptive importance samplingCross-entropy methodModel selectionNetwork growth modelsPlackett-Luce modelPreferential attachment

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Area of Science:

  • Complex Systems
  • Network Science
  • Computational Statistics

Background:

  • Network growth models like Preferential Attachment are crucial for understanding complex systems.
  • Statistical inference and model selection for these networks are computationally challenging.
  • Existing ad hoc methods yield uncertain results for model comparison.

Purpose of the Study:

  • To develop a robust statistical method for estimating likelihoods of network growth models.
  • To enable reliable model selection and comparison within complex network analysis.
  • To address the computational difficulties in analyzing generative network models.

Main Methods:

  • Developed an Adaptive Importance Sampling (AIS) algorithm tailored for network growth models.
  • Utilized the Plackett-Luce model of rankings as a family of importance distributions.
  • Incorporated the Cross-Entropy Method with a Minimum Description Length-inspired correction for iterative refinement.

Main Results:

  • The proposed AIS algorithm effectively estimates likelihoods for network growth models.
  • Empirical results demonstrate strong performance for the Preferential Attachment model.
  • The method shows promise compared to established techniques like Annealed Importance Sampling.

Conclusions:

  • The novel Adaptive Importance Sampling algorithm provides a powerful tool for network growth model analysis.
  • This approach enhances statistical inference and model selection in complex network science.
  • The technique offers a computationally feasible and reliable alternative to existing methods.