OxTalks will soon move to the new Halo platform and will become 'Oxford Events.' There will be a need for an OxTalks freeze. This was previously planned for Friday 14th November – a new date will be shared as soon as it is available (full details will be available on the Staff Gateway).
In the meantime, the OxTalks site will remain active and events will continue to be published.
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Spatial random graphs provide an important framework for the analysis of relations and interactions in networks. In particular, the random geometric graph has been intensively studied and applied in various frameworks like network modeling or percolation theory.
In this talk we focus on approximation results for a generalization of the random geometric graph that consists of vertices given by a Gibbs process and (conditionally) independent edges generated from a connection function. Using Stein’s method, we compare this graph model with general spatial random graphs with respect to general integral probability metrics, providing concrete rates in the case of a suitable Wasserstein distance. We then briefly present an application of our results in the context of differential privacy. Finally, we describe how associated kernel Stein discrepancies can be used for goodness-of-fit testing in the framework of point processes and, as future work, spatial random graphs.
