Belief Propagation, Robust Reconstruction and Optimal Recovery of Block Models
published: July 15, 2014, recorded: June 2014, views: 2895
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
We consider the problem of reconstructing sparse symmetric block models with two blocks and connection probabilities a/n and b/n for inter- and intra-block edge probabilities respectively. It was recently shown that one can do better than a random guess if and only if (a−b)2>2(a+b). Using a variant of Belief Propagation, we give a reconstruction algorithm that is optimal in the sense that if (a−b)2>C(a+b) for some constant C then our algorithm maximizes the fraction of the nodes labelled correctly. Along the way we prove some results of independent interest regarding robust reconstruction for the Ising model on regular and Poisson trees.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !