Liu, S.; Pu, T.; Zeng, L.; Wang, Y.; Cheng, H.; Liu, Z. Reinforcement Learning-based Network Dismantling by Targeting Maximum Degree Nodes in the Giant Connected Component. Preprints2024, 2024081397. https://doi.org/10.20944/preprints202408.1397.v1
APA Style
Liu, S., Pu, T., Zeng, L., Wang, Y., Cheng, H., & Liu, Z. (2024). Reinforcement Learning-based Network Dismantling by Targeting Maximum Degree Nodes in the Giant Connected Component. Preprints. https://doi.org/10.20944/preprints202408.1397.v1
Chicago/Turabian Style
Liu, S., Haoxiang Cheng and Zhong Liu. 2024 "Reinforcement Learning-based Network Dismantling by Targeting Maximum Degree Nodes in the Giant Connected Component" Preprints. https://doi.org/10.20944/preprints202408.1397.v1
Abstract
Tackling the intricacies of network dismantling in complex systems poses significant challenges. This task has relevance across various practical domains, yet traditional approaches focus primarily on singular metrics, such as the number of nodes in the Giant Connected Component (GCC) or average pairwise connectivity. In contrast, we propose a unique metric that concurrently targets nodes with the highest degree and reduces the GCC size. Given the NP-hard nature of optimizing this metric, we introduce MaxShot, an innovative end-to-end solution that leverages graph representation learning and reinforcement learning. Through comprehensive evaluations on both synthetic and real-world datasets, our method consistently outperforms leading benchmarks in accuracy and efficiency. These results highlight MaxShot’s potential as a superior approach to address the network dismantling problem effectively.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
