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
Citation Metrics
Citations
0
Influential Citations
0
Reference Count
0

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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.

Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study
Medicine and Health

Predictive model of castration resistance in advanced prostate cancer by machine learning using genetic and clinical data: KYUCOG-1401-A study

M. Shiota, S. Nemoto, et al.

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