  We consider the Steiner tree problem on graphs where we are given a set of
nodes and the goal is to find a tree sub-graph of minimum weight that contains
all nodes in the given set, potentially including additional nodes. This is a
classical NP-hard combinatorial optimisation problem. In recent years, a
machine learning framework called learning-to-prune has been successfully used
for solving a diverse range of combinatorial optimisation problems. In this
paper, we use this learning framework on the Steiner tree problem and show that
even on this problem, the learning-to-prune framework results in computing
near-optimal solutions at a fraction of the time required by commercial ILP
solvers. Our results underscore the potential of the learning-to-prune
framework in solving various combinatorial optimisation problems.
