  Social science studies dealing with control in networks typically resort to
heuristics or describing the static control distribution. Optimal policies,
however, require interventions that optimize control over a socioeconomic
network subject to real-world constraints. We integrate optimisation tools from
deep-learning with network science into a framework that is able to optimize
such interventions in real-world networks. We demonstrate the framework in the
context of corporate control, where it allows to characterize the vulnerability
of strategically important corporate networks to sensitive takeovers, an
important contemporaneous policy challenge. The framework produces insights
that are relevant for governing real-world socioeconomic networks, and opens up
new research avenues for improving our understanding and control of such
complex systems.
