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.
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Acknowledgements
The Italian Ministry for Research and Education (MIUR) through Research Program PRIN 2017 (2017CWMF93), project ‘Advanced Network Control of Future Smart Grids - VECTORS’.
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Grassia, M., Mangioni, G. (2023). CoreGDM: Geometric Deep Learning Network Decycling and Dismantling. In: Teixeira, A.S., Botta, F., Mendes, J.F., Menezes, R., Mangioni, G. (eds) Complex Networks XIV. CompleNet 2023. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-28276-8_8
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