mlsanityData to 7 Oct 2026

A Stein Goodness of fit Test for Exponential Random Graph Models

arXiv 2103.00580 ↗First posted 28 Feb 2021stat.ME stat.MLcited by 2

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Abstract (arXiv)

We propose and analyse a novel nonparametric goodness of fit testing procedure for exchangeable exponential random graph models (ERGMs) when a single network realisation is observed. The test determines how likely it is that the observation is generated from a target unnormalised ERGM density. Our test statistics are derived from a kernel Stein discrepancy, a divergence constructed via Steins method using functions in a reproducing kernel Hilbert space, combined with a discrete Stein operator for ERGMs. The test is a Monte Carlo test based on simulated networks from the target ERGM. We show theoretical properties for the testing procedure for a class of ERGMs. Simulation studies and real network applications are presented.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 74 references that are in the map. First posted on arXiv 28 Feb 2021.

Builds on

23 of 74 references.

References · 23

  1. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  2. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 360
  3. 2016A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluationcited by 236
  4. 2019A Survey on Graph Kernelscited by 148
  5. 2016A Kernel Test of Goodness of Fitcited by 143
  6. 2015Measuring Sample Quality with Stein's Methodcited by 94
  7. 2019Minimum Stein Discrepancy Estimatorscited by 57
  8. 2016On Valid Optimal Assignment Kernels and Applications to Graph Classificationcited by 49
  9. 2017A Linear-Time Kernel Goodness-of-Fit Testcited by 41
  10. 2020Stochastic Stein Discrepanciescited by 30
  11. 2014A Wild Bootstrap for Degenerate Kernel Testscited by 28
  12. 2020Testing Goodness of Fit of Conditional Density Models with Kernelscited by 19
11 more
  1. 2019A Kernel Stein Test for Comparing Latent Variable Modelscited by 18
  2. 2016An Adaptive Test of Independence with Analytic Kernel Embeddingscited by 13
  3. 2020A Stein Goodness-of-fit Test for Directional Distributionscited by 13
  4. 2020Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Datacited by 12
  5. 2018Informative Features for Model Comparisoncited by 9
  6. 2013Subsampling bootstrap of count features of networkscited by 8
  7. 2017Exponential random graphs behave like mixtures of stochastic block modelscited by 5
  8. 2018Optimal Single Sample Tests for Structured versus Unstructured Network Datacited by 5
  9. 2018A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Networkcited by 5
  10. 2014Spectral goodness of fit for network modelscited by 3
  11. 2019Selection of Exponential-Family Random Graph Models via Held-Out Predictive Evaluation (HOPE)cited by 1

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Cited by year

Citing papers per year: 2021: 2.

2 papers.

  1. 2021Composite Goodness-of-fit Tests with Kernelscited by 16
  2. 2021Spectral goodness-of-fit tests for complete and partial network datacited by 0