Denoising and Untangling Graphs Using Degree Priors
This paper addresses the problem of untangling hidden graphs from a set of noisy detections of undirected edges. We present a model of the generation of the observed graph that includes degree-based structure priors on the hidden graphs. Exact inference in the model is intractable; we present an efficient approximate inference algorithm to compute edge appearance posteriors. We evaluate our model and algorithm on a biological graph inference problem.