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Methods of Simulating Networks from Exponential Random Graph Models

  • Author(s): Yang, Suoyi
  • Advisor(s): Handcock, Mark S
  • et al.
Abstract

The exponential random graph models are a family of statistical models that are often used for analyzing networks. The most common method of simulating networks from these models is using the Metropolis-Hastings algorithm. However, the Metropolis-Hastings algorithm has certain disadvantages such as long mixing times and issues of degeneracy. As a result, the goal of this paper is to look for an alternative method of simulating networks from exponential random graph models that can improve upon the drawbacks of the Metropolis-Hastings algorithm. The three alternative methods explored in this paper are the Swendsen-Wang algorithm, the Gibbs sampler with Swendsen-Wang algorithm, and the iterative sampling with spectral clustering algorithm. Out of these three, the iterative sampling with spectral clustering ultimately proved to be the most viable algorithm. The rest of the paper focuses on analyzing the network simulations generated using this method.

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