Denoising and Untangling Graphs Using Degree Priors

The inference of hidden graphs from noisy edge appearance data is an important problem with obvious practical application. For example, biologists are currently building networks of all the physical protein-protein interactions (PPI) that occur in particular organisms. The importance of this enterprise is commensurate with its scale: a completed network would be as valuable as a completed genome sequence, and because each organism contains thousands of difierent types of proteins, there are millions of possible types of interactions. However, scalable experimental meth- ods for detecting interactions are noisy, generating many false detections. Motivated by this application, we formulate the general problem of inferring hidden graphs as probabilistic inference in a graphical model, and we introduce an e–cient algorithm that approximates the posterior probability that an edge is present.