Reconstruction for Models on Random Graphs

Consider a collection of random variables attached to the vertices of a graph. The reconstruction problem requires to estimate one of them given ‘far away’ observations. Several theoretical results (and simple algorithms) are available when their joint probability distribution is Markov with respect to a tree. In this paper we consider the case of sequences of random graphs that converge locally to trees. In particular, we develop a sufficient condition for the tree and graph reconstruction problem to coincide. We apply such condition to colorings of random graphs. Further, we characterize the behavior of Ising models on such graphs, both with attractive and random interactions (respectively, ‘ferromagnetic’ and ‘spin glass’).

• Type: Conference
• Year: 2007
• Where: FOCS
• Authors: Antoine Gerschenfeld, Andrea Montanari
• Keywords: FOCS 2007, Random Graphs, Random Variables, Reconstruction Problem, Theoretical Computer Science