A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy
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Abstract (arXiv)
Complex data are often represented as a graph, which in turn can often be viewed as a realisation of a random graph, such as an inhomogeneous random graph model (IRG). For general fast goodness-of-fit tests in high dimensions, kernelised Stein discrepancy (KSD) tests are a powerful tool. Here, we develop a KSD-type test for IRG models that can be carried out with a single observation of the network. The test applies to a network of any size, but is particularly interesting for small networks for which asymptotic tests are not warranted. We also provide theoretical guarantees.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 51 references that are in the map. First posted on arXiv 27 May 2025.
Builds on
11 of 51 references.
References · 11
- 2016A Kernel Test of Goodness of Fitcited by 143
- 2017Cooperative Game Theory Approaches for Network Partitioningcited by 135
- 2015Measuring Sample Quality with Stein's Methodcited by 94
- 2014A goodness-of-fit test for stochastic block modelscited by 20
- 2019A Kernel Stein Test for Comparing Latent Variable Modelscited by 18
- 2021Online Learning of Network Bottlenecks via Minimax Pathscited by 9
- 2017Using Maximum Entry-Wise Deviation to Test the Goodness-of-Fit for Stochastic Block Modelscited by 8
- 2016Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodelscited by 2
- 2023A spectral based goodness-of-fit test for stochastic block modelscited by 2
- 2017A Mixed-Membership Model for Social Network Clusteringcited by 1
- 2025Network Goodness-of-Fit for the block-model familycited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Nystr"om Kernel Stein Discrepancy Testscited by 2
- 2026Nystr"om Kernel Stein Discrepancy Tests24 May 2026