  Stochastic networks are a plausible representation of the relational
information among entities in dynamic systems such as living cells or social
communities. While there is a rich literature in estimating a static or
temporally invariant network from observation data, little has been done toward
estimating time-varying networks from time series of entity attributes. 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. We
report promising results on recovering simulated time-varying networks. For
real data sets, we reverse engineer the latent sequence of temporally rewiring
political networks between Senators from the US Senate voting records and the
latent evolving regulatory networks underlying 588 genes across the life cycle
of Drosophila melanogaster from the microarray time course.
