A Stein goodness-of-test for exponential random graph models

Conference item

We propose and analyse a novel nonparametric goodness-of-fit testing procedure for exchangeable exponential random graph model (ERGM) 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 of kernel Stein discrepancy, a divergence constructed via Stein’s method using functions from a reproducing kernel Hilbert space (RKHS), combined with a discrete Stein operator for ERGMs. The test is a Monte Carlo test using simulated networks from the target ERGM. We show theoretical properties for the testing procedure w.r.t a class of ERGMs. Simulation studies and real network applications are presented.

Authors: Xu, W; Reinert, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Pages: 415-423
• Series: Proceedings of Machine Learning Research
• Series number: 130
• Publication date: 2021-03-18
• Acceptance date: 2021-01-23
• Event title: 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021)
• Event location: Virtual event
• Event website: https://aistats.org/aistats2021/index.html
• Event start date: 2021-04-13
• Event end date: 2021-04-15
• Language: English
• Keywords: FFR