Before revision:  In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as  standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks.
After revision:  In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks.