We consider the problem of recovering the graph structure of a “hub-networked” Ising model given i.i.d. samples, under high-dimensional settings, where number of nodes p could be potentially larger than the number of samples n. By a “hub-networked” graph, we mean a graph with a few “hub nodes” with very large degrees. State of the art estimators for Ising models have a sample complexity that sc...