  We consider the problem of learning the underlying graph of an unknown Ising
model on p spins from a collection of i.i.d. samples generated from the model.
We suggest a new estimator that is computationally efficient and requires a
number of samples that is near-optimal with respect to previously established
information-theoretic lower-bound. Our statistical estimator has a physical
interpretation in terms of "interaction screening". The estimator is consistent
and is efficiently implemented using convex optimization. We prove that with
appropriate regularization, the estimator recovers the underlying graph using a
number of samples that is logarithmic in the system size p and exponential in
the maximum coupling-intensity and maximum node-degree.
