A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy

Anum Fatima, Gesine Reinert
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:1225-1233, 2026.

Abstract

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 networks of any size, but is particularly relevant for small networks for which asymptotic tests are not warranted. We also provide theoretical guarantees.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-fatima26a, title = { A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy }, author = {Fatima, Anum and Reinert, Gesine}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {1225--1233}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/fatima26a/fatima26a.pdf}, url = {https://proceedings.mlr.press/v300/fatima26a.html}, abstract = { 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 networks of any size, but is particularly relevant for small networks for which asymptotic tests are not warranted. We also provide theoretical guarantees. } }
Endnote
%0 Conference Paper %T A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy %A Anum Fatima %A Gesine Reinert %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-fatima26a %I PMLR %P 1225--1233 %U https://proceedings.mlr.press/v300/fatima26a.html %V 300 %X 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 networks of any size, but is particularly relevant for small networks for which asymptotic tests are not warranted. We also provide theoretical guarantees.
APA
Fatima, A. & Reinert, G.. (2026). A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:1225-1233 Available from https://proceedings.mlr.press/v300/fatima26a.html.

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