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

Computer Science

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

M. Grassia, M. D. Domenico, et al.

This groundbreaking research by Marco Grassia, Manlio De Domenico, and Giuseppe Mangioni delves into the power of machine learning for dismantling complex networks. The innovative GDM framework not only identifies key patterns but also offers predictive insights into systemic risks and the likelihood of system collapse.

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Playback language: English
Abstract
This paper explores the use of machine learning to efficiently dismantle complex networks and predict their disintegration. A machine learning framework, GDM (Graph Dismantling with Machine learning), is developed and trained on smaller networks to identify topological patterns crucial for efficient dismantling of larger networks. The model outperforms existing heuristics and provides a quantitative early-warning signal for systemic risk, predicting the probability of system collapse.
Publisher
NATURE COMMUNICATIONS
Published On
Aug 31, 2021
Authors
Marco Grassia, Manlio De Domenico, Giuseppe Mangioni
Tags
machine learning
network dismantling
system collapse
topological patterns
predictive model

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