\dm_csml_event_details UCL ELLIS

AgraSSt Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators


Wenkai Xu


University of Oxford


Friday, 14 October 2022




Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH



Event series

DeepMind/ELLIS CSML Seminar Series


We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators which may not be available in explicit forms. In particular, AgraSSt can be used to determine whether a learned graph generating process is capable of generating graphs which resemble a given input graph. Inspired by Stein operators for random graphs, the key idea of AgraSSt is the construction of a kernel discrepancy based on an operator obtained from the graph generator. AgraSSt can provide interpretable criticisms for a graph generator training procedure and help identify reliable sample batches for downstream tasks. We give theoretical guarantees for a broad class of random graph models. We provide empirical results on both synthetic input graphs with known graph generation procedures, and real-world input graphs that the state-of-the-art (deep) generative models for graphs are trained on.


Wenkai is a postdoc research associate in statistics at the Department of Statistics, University of Oxford. His research interest includes Stein’s method, kernel method, hypothesis testing and statistical inference beyond Euclidean data. His current work focuses on characterising and assessing random graph models and deep generative models via Stein’s method. He completed his Ph.D. in the Gatsby Computational Neuroscience Unit under the supervision of Prof. Arthur Gretton and Prof. Aapo Hyvarinen.