Network dismantling deals with the removal of nodes or edges to disrupt the largest connected component of a network. In this work we introduce CoreGDM, a trainable algorithm for network dismantling via node-removal. The approach is based on Geometric Deep Learning and that merges the Graph Dismantling Machine (GDM) [19] framework with the CoreHD [40] algorithm, by attacking the 2-core of the network using a learnable score function in place of the degree-based one. Extensive experiments on fifteen real-world networks show that CoreGDM outperforms the original GDM formulation and the other state-of-the-art algorithms, while also being more computationally efficient.

CoreGDM: Geometric Deep Learning Network Decycling and Dismantling

Grassia M.;Mangioni G.
2023-01-01

Abstract

Network dismantling deals with the removal of nodes or edges to disrupt the largest connected component of a network. In this work we introduce CoreGDM, a trainable algorithm for network dismantling via node-removal. The approach is based on Geometric Deep Learning and that merges the Graph Dismantling Machine (GDM) [19] framework with the CoreHD [40] algorithm, by attacking the 2-core of the network using a learnable score function in place of the degree-based one. Extensive experiments on fifteen real-world networks show that CoreGDM outperforms the original GDM formulation and the other state-of-the-art algorithms, while also being more computationally efficient.
2023
978-3-031-28275-1
978-3-031-28276-8
Geometric deep learning
Machine learning
Network dismantling
Site percolation
Supervised learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/558202
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