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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23840
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dc.contributor.authorMa, L-
dc.contributor.authorShao, Z-
dc.contributor.authorLi, X-
dc.contributor.authorLin, Q-
dc.contributor.authorLi, J-
dc.contributor.authorLeung, VCM-
dc.contributor.authorNandi, A-
dc.date.accessioned2021-12-29T19:16:13Z-
dc.date.available2021-12-29T19:16:13Z-
dc.date.issued2022-01-13-
dc.identifier.citationMa, L., Shao, Z., Li, X., Lin, Q., Li, J., Leung, V.C.M. and Nandi, A., (2022) 'Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning', IEEE Transactions on Emerging Topics in Computational Intelligence, 0 (in press), pp. 1-14. doi: 10.1109/TETCI.2021.3136643.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/23840-
dc.description.sponsorshipJoint Funds of the National Natural Science Foundation of China under Key Program under Grant U1713212; National Natural Science Foundation of China under Grants 61672358, 61572330, 61772393 and 61836005; Natural Science Foundation of Guangdong Province under grant 2017A030313338.en_US
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62173236 61803269 61876110 61806130 61976142 U1713212 62072315 61836005); 10.13039/501100003453-Natural Science Foundation of Guangdong Province (Grant Number: 2020A1515010790); Technology Research Project of Shenzhen City (Grant Number: JCYJ20190808174801673).-
dc.format.extent1 - 14-
dc.format.mediumElectronic-
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.rights© 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectcomplex networks,en_US
dc.subjectinfluence maximizationen_US
dc.subjectdeep reinforcement learningen_US
dc.subjectevolutionary algorithmen_US
dc.subjectoptimizationen_US
dc.titleInfluence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learningen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1109/TETCI.2021.3136643-
dc.relation.isPartOfIEEE Transactions on Emerging Topics in Computational Intelligence-
pubs.publication-statusPublished online-
pubs.volume0-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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