Machine learning dismantling and early-warning signals of disintegration in complex systems.

ABSTRACT: 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 mach

SUBMITTER: Grassia M 

PROVIDER: S-EPMC8408155 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature