Information-theoretic limits of selecting binary graphical models in high dimensions

The problem of graphical model selection is to correctly estimate the graph structure of a Markov random field given samples from the underlying distribution. We analyze the information-theoretic limitations of the problem of graph selection for binary Markov random fields under high-dimensional scaling, in which the graph size $p$ and the number of edges $k$, and/or the maximal node degree $d$ are allowed to increase to infinity as a function of the sample size $n$. For pairwise binary Markov random fields, we derive both necessary and sufficient conditions for correct graph selection over the class $\mathcal{G}{p,k}$ of graphs on $p$ vertices with at most $k$ edges, and over the class $\mathcal{G}{p,d}$ of graphs on $p$ vertices with maximum degree at most $d$. For the class $\mathcal{G}{p, k}$, we establish the existence of constants $c$ and $c'$ such that if $\numobs < c k \log p$, any method has error probability at least 1/2 uniformly over the family, and we demonstrate a graph decoder that succeeds with high probability uniformly over the family for sample sizes $\numobs > c' k² \log p$. Similarly, for the class $\mathcal{G}{p,d}$, we exhibit constants $c$ and $c'$ such that for $n < c d² \log p$, any method fails with probability at least 1/2, and we demonstrate a graph decoder that succeeds with high probability for $n > c' d³ \log p$.