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12-03-2020 | Original Article | Issue 9/2020

International Journal of Machine Learning and Cybernetics 9/2020

A novel learning-based approach for efficient dismantling of networks

Journal:
International Journal of Machine Learning and Cybernetics > Issue 9/2020
Authors:
Changjun Fan, Li Zeng, Yanghe Feng, Guangquan Cheng, Jincai Huang, Zhong Liu
Important notes
Changjun Fan and Li Zeng these authors have contributed equally to this work.

Publisher's Note

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Abstract

Dismantling of complex networks is a problem to find a minimal set of nodes in which the removal leaves the network broken into connected components of sub-extensive size. It has a wide spectrum of important applications, including network immunization and network destruction. Due to its NP-hard computational complexity, this problem cannot be solved exactly with polynomial time. Traditional solutions, including manually-designed and considerably sub-optimal heuristic algorithms, and accurate message-passing ones, all suffer from low efficiency in large-scale problems. In this paper, we introduce a simple learning-based approach, CoreGQN, which seeks to train an agent that is able to smartly choose nodes that would accumulate the maximum rewards. CoreGQN is trained by hundreds of thousands self-plays on small synthetic graphs, and can then be able to generalize well on real-world networks across different types with different scales. Extensive experiments demonstrate that CoreGQN performs on par with the state-of-art algorithms at greatly reduced computational costs, suggesting that CoreGQN should be the better choice for practical network dismantling purposes.

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