Deep Reinforcement Learning for Network Dismantling: A K-Core Based Approach

Tianle Pu, Li Zeng and Chao Chen ()
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Tianle Pu: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Li Zeng: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Chao Chen: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 8, 1-12

Abstract: Network dismantling is one of the most challenging problems in complex systems. This problem encompasses a broad array of practical applications. Previous works mainly focus on the metrics such as the number of nodes in the Giant Connected Component (GCC), average pairwise connectivity, etc. This paper introduces a novel metric, the accumulated 2-core size, for assessing network dismantling. Due to the NP-hard computational complexity of this problem, we propose SmartCore, an end-to-end model for minimizing the accumulated 2-core size by leveraging reinforcement learning and graph neural networks. Extensive experiments across synthetic and real-world datasets demonstrate SmartCore’s superiority over existing methods in terms of both accuracy and speed, suggesting that SmartCore should be a better choice for the network dismantling problem in practice.

Keywords: complex networks; network dismantling; graph neural network; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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