Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation

We consider the problem of fitting autoregressive graph generative models via maximum likelihood estimation (MLE). MLE is intractable for graph autoregressive models because the nodes in a graph can be arbitrarily reordered; thus the exact likelihood involves a sum over all possible node orders leading to the same graph. In this work, we fit the graph models by maximizing a variational bound, which is built by first deriving the joint probability over the graph and the node order of the …

autoregressive models generative generative models graph likelihood maximum likelihood estimation mle node orders probability process through work