Rewiring complex networks to achieve cluster synchronization using graph convolution networks with reinforcement learning

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dc.contributor.authorZou, Mengbang
dc.contributor.authorGuo, Weisi
dc.contributor.authorChu, Kai-Fung
dc.date.accessioned2024-06-19T09:37:07Z
dc.date.available2024-06-19T09:37:07Z
dc.date.issued2024-06-10
dc.description.abstractSynchronization on complex networks depends on a myriad of factors such as embedded dynamics, initial conditions, network structure, etc. Current literature simplifies analysis of cluster synchronization leveraging conditions on network topology such as input-equivalence, network symmetries, etc., of which external equitable partition (EEP) is one of the most relaxed conditions. One practical problem is that for a dynamic system, how to alter a network to reach arbitrary achievable cluster synchronization and remaining faithful to the original structure. To solve this problem, we represent graph dynamics in Graph Convolution Network (GCN) modules that sit within an Actor-Critic Reinforcement Learning (AC-RL) framework under the condition of EEP. This allows the framework to select a good policy to sequentially rewire the network, where the sequence of moves matters. We test our method on two types of high-dimensional networked systems, Rossler dynamic networks and Hindmarsh-Rose neuronal circuits, with different network sizes. Our research opens up a way for the discovery of achievable cluster synchronization configurations by altering the network structure in any given networked dynamics.en_UK
dc.description.sponsorshipEPSRC CHEDDAR: Communications Hub For Empowering Distributed ClouD Computing Applications And Research (Grant Number: EP/X040518/1 and EP/Y037421/1).en_UK
dc.identifier.citationZou M, Guo W, Chu K-F. (2024) Rewiring complex networks to achieve cluster synchronization using graph convolution networks with reinforcement learning. IEEE Transactions on Network Science and Engineering. Available online 10 June 2024en_UK
dc.identifier.eissn2327-4697
dc.identifier.issn2334-329X
dc.identifier.urihttps://doi.org/10.1109/TNSE.2024.3410694
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22515
dc.language.isoen_UKen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectcomplex networken_UK
dc.subjectsynchronizabilityen_UK
dc.subjectreinforcement learningen_UK
dc.subjectgraph neural networken_UK
dc.titleRewiring complex networks to achieve cluster synchronization using graph convolution networks with reinforcement learningen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-05-29

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