ArticleNature communications2021
Machine learning dismantling and early-warning signals of disintegration in complex systems.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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Who cites it
14 citing papers in PubMed.
- A game-theoretic attack-defense framework for the study of network resilience.Nature communications · 2026Article
- Temporal self-similarity reveals percolation universality classes in complex networks.Nature communications · 2026Article
- Optimal dismantling of directed networks.Nature communications · 2026Article
- Machine-enhanced reconstruction of functional connectomes unravels discriminative brain sub-systems in health and disease.Scientific reports · 2026Article
- Design of robust networks via reinforcement learning prompts the emergence of multi-backbones.Nature communications · 2026Article
- Interpretable early warnings using machine learning in an online game-experiment.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- Integrating graph and reinforcement learning for vaccination strategies in complex networks.Scientific reports · 2024Article
- Article
- Exploring the landscape of dismantling strategies based on the community structure of networks.Scientific reports · 2023Article
- Inferring origin-destination distribution of agent transfer in a complex network using deep gated recurrent units.Scientific reports · 2023Article
- From the origin of life to pandemics: emergent phenomena in complex systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion.Computational intelligence and neuroscience · 2022Article
- Machine learning dismantling and early-warning signals of disintegration in complex systems.Nature communications · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.