GDM: Graph Dismantling with Machine Learning
Code, training and test networks, and pre-trained models from the 2021 paper on machine learning dismantling and early-warning signals.
About the resource
This repository accompanies the paper:
M. Grassia, M. De Domenico, and G. Mangioni. Machine learning dismantling and early-warning signals of disintegration in complex systems. Nature Communications 12, 5190 (2021).
A graph neural network trained to dismantle small synthetic networks is used to attack large real-world ones, and its predictions give an early-warning signal of the network’s collapse. The repository holds the scripts, the networks used for training and testing, and the pre-trained models.
Use the resource
Setup and reproduction instructions are in the repository. Only part of the real-world test networks is included, because the original data come under different licences.
Citation and licence
Please cite the paper, and the archived release when you use the code. The code is released under GPLv3.