Application of information-percolation method to reconstruction problems on graphs.

In this paper we propose a method of proving impossibility results based on applying strong data-processing inequalities to estimate mutual information between sets of variables forming certain Markov random fields. The end result is that mutual information between two `far awayu0027 (as measured by the graph distance) variables is bounded by the probability of existence of open path in a bond-percolation problem on the same graph. Furthermore, stronger bounds can be obtained by establishing mutual comparison results with an erasure model on the same graph, with erasure probabilities given by the contraction coefficients. As application, we show that our method gives sharp threshold for partially recovering a rank-one perturbation of a random Gaussian matrix (spiked Wigner model), recovers (and generalizes) the best known upper bound on noise-level for group synchronization due to Abbe and Boix, and establishes new impossibility result for a $k$-community detection (stochastic block model).