  We present a brief introduction to a flexible, general network inference
framework which models data as a network space, sampled to optimize network
structure to a particular task. We introduce a formal problem statement related
to influence maximization in networks, where the network structure is not given
as input, but learned jointly with an influence maximization solution.
