Immunization and targeted destruction of networks using explosive percolation
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Abstract (arXiv)
A new method (`explosive immunization' (EI)) is proposed for immunization and targeted destruction of networks. It combines the explosive percolation (EP) paradigm with the idea of maintaining a fragmented distribution of clusters. The ability of each node to block the spread of an infection (or to prevent the existence of a large cluster of connected nodes) is estimated by a score. The algorithm proceeds by first identifying low score nodes that should not be vaccinated/destroyed, analogously to the links selected in EP if they do not lead to large clusters. As in EP, this is done by selecting the worst node (weakest blocker) from a finite set of randomly chosen `candidates'. Tests on several real-world and model networks suggest that the method is more efficient and faster than any existing immunization strategy. Due to the latter property it can deal with very large networks.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
4 papers.
- 2021Machine learning dismantling and early-warning signals of disintegration in complex systemscited by 14
- 2025Advancing network resilience theories with symbolized reinforcement learningcited by 0
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- 2026Network World Models as Environments for Algorithm Design on Complex Systemscited by 0
- 2026Network World Models as Environments for Algorithm Design on Complex Systems1 Oct 2026
- 2025Learning Network Dismantling Without Handcrafted Inputs1 Aug 2025
- 2025Advancing network resilience theories with symbolized reinforcement learning4 Jul 2025
- 2021Machine learning dismantling and early-warning signals of disintegration in complex systems7 Jan 2021