logo
ResearchBunny Logo
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.

00:00
00:00
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

Related Publications

Explore these studies to deepen your understanding of the subject.

Early warning of trends in commercial wildlife trade through novel machine-learning analysis of patent filing
Biology

Early warning of trends in commercial wildlife trade through novel machine-learning analysis of patent filing

A. Hinsley, D. W. S. Challender, et al.

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review
Medicine and Health

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

J. Zhang, F. Zhong, et al.

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals
Engineering and Technology

Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals

S. W. Rabkin, A. Cataldo, et al.

Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms
Linguistics and Languages

Stylistic and linguistic variations in compliments: an empirical analysis of children's gender schema development with machine learning algorithms

X. Liao and Y. Zhang

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny