Fast and simple decycling and dismantling of networks
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Abstract (arXiv)
Decycling and dismantling of complex networks are underlying many important applications in network science. Recently these two closely related problems were tackled by several heuristic algorithms, simple and considerably sub-optimal, on the one hand, and time-consuming message-passing ones that evaluate single-node marginal probabilities, on the other hand. In this paper we propose a simple and extremely fast algorithm, CoreHD, which recursively removes nodes of the highest degree from the 2-core of the network. CoreHD performs much better than all existing simple algorithms. When applied on real-world networks, it achieves equally good solutions as those obtained by the state-of-art iterative message-passing algorithms at greatly reduced computational cost, suggesting that CoreHD should be the algorithm of choice for many practical purposes.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
4 papers.
- 2021Machine learning dismantling and early-warning signals of disintegration in complex systemscited by 14
- 2024Identify Critical Nodes in Complex Network with Large Language Modelscited by 4
- 2025Advancing network resilience theories with symbolized reinforcement learningcited by 0
- 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
- 2025Advancing network resilience theories with symbolized reinforcement learning4 Jul 2025
- 2024Identify Critical Nodes in Complex Network with Large Language Models1 Mar 2024
- 2021Machine learning dismantling and early-warning signals of disintegration in complex systems7 Jan 2021