  From physics to engineering, biology and social science, natural and
artificial systems are characterized by interconnected topologies whose
features - e.g., heterogeneous connectivity, mesoscale organization, hierarchy
- affect their robustness to external perturbations, such as targeted attacks
to their units. Identifying the minimal set of units to attack to disintegrate
a complex network, i.e. network dismantling, is a computationally challenging
(NP-hard) problem which is usually attacked with heuristics. Here, we show that
a machine trained to dismantle relatively small systems is able to identify
higher-order topological patterns, allowing to disintegrate large-scale social,
infrastructural and technological networks more efficiently than human-based
heuristics. Remarkably, the machine assesses the probability that next attacks
will disintegrate the system, providing a quantitative method to quantify
systemic risk and detect early-warning signals of system's collapse. This
demonstrates that machine-assisted analysis can be effectively used for policy
and decision making to better quantify the fragility of complex systems and
their response to shocks.
