Reinforcement Learning-Based Network Dismantling by Targeting Maximum-Degree Nodes in the Giant Connected Component

Autor:	Shixuan Liu, Tianle Pu, Li Zeng, Yunfei Wang, Haoxiang Cheng, Zhong Liu
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	complex networks network dismantling graph representation learning reinforcement learning Mathematics QA1-939
Zdroj:	Mathematics, Vol 12, Iss 17, p 2766 (2024)
Druh dokumentu:	article
ISSN:	12172766 2227-7390
DOI:	10.3390/math12172766
Popis:	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 the 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 effectively addressing the network dismantling problem.
Databáze:	Directory of Open Access Journals
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