2021/01/07 by Marco Grassia, Manlio De Domenico, Giuseppe Mangioni · 1 citation
Physics and Astronomy · Computer Science · #physics.soc-ph #cs.LG
paper · pdf · doi:10.1038/s41467-021-25485-8
published as Nat Commun 12, 5190 (2021) · 18 pages, 5 figures. Supplementary materials: 35 pages, 10 figures, 4 tables
arxiv created 2021/01/07 · arxiv updated 2021/09/01
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.