Stein’s Method for Exponential Random Graph Models and Kernelized Goodness of Fit
Gesine Reinert – University of Oxford
Exponential random graph models are a key tool in network analysis but due to an intractable normalising constant are difficult to manipulate. In this talk we shall use Stein’s method to approximate these models by Bernoulli random graphs in “high temperature” regimes.
For assessing the goodness of fit of a model, often independent replicas are assumed. When the data are given in the form of a network, usually there is only one network available. If the data are hypothesised to come from an exponential random graph model, the likelihood cannot be calculated explicitly. Using a Stein operator for these models we introduce a kernelized goodness of fit test and illustrate its performance.
This talk is based on joint work with Nathan Ross and with Wenkai Xu.