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http://bura.brunel.ac.uk/handle/2438/23840Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ma, L | - |
| dc.contributor.author | Shao, Z | - |
| dc.contributor.author | Li, X | - |
| dc.contributor.author | Lin, Q | - |
| dc.contributor.author | Li, J | - |
| dc.contributor.author | Leung, VCM | - |
| dc.contributor.author | Nandi, AK | - |
| dc.date.accessioned | 2021-12-29T19:16:13Z | - |
| dc.date.available | 2021-12-29T19:16:13Z | - |
| dc.date.issued | 2022-01-13 | - |
| dc.identifier | ORCID iDs: Lijia Ma https://orcid.org/0000-0002-1201-8051; Qiuzhen Lin https://orcid.org/0000-0003-2415-0401; Jianqiang Li https://orcid.org/0000-0002-2208-962X; Victor C. M. Leung https://orcid.org/0000-0003-3529-2640; Asoke K. Nandi https://orcid.org/0000-0001-6248-2875. | - |
| dc.identifier.citation | Ma, L. et al. (2022) 'Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning', IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (4), pp. 995 - 1009. doi: 10.1109/TETCI.2021.3136643. | en_US |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23840 | - |
| dc.description.sponsorship | Joint 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.sponsorship | 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62173236, 61803269, 61876110, 61806130, 61976142, U1713212, 62072315 and 61836005); 10.13039/501100003453-Natural Science Foundation of Guangdong Province (Grant Number: 2020A1515010790); Technology Research Project of Shenzhen City (Grant Number: JCYJ20190808174801673). | - |
| dc.format.extent | 995 - 1009 | - |
| dc.format.medium | Electronic | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights | Copyright © 2021 Institute of Electrical and Electronics Engineers (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 by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.subject | complex networks, | en_US |
| dc.subject | influence maximization | en_US |
| dc.subject | deep reinforcement learning | en_US |
| dc.subject | evolutionary algorithm | en_US |
| dc.subject | optimization | en_US |
| dc.title | Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning | en_US |
| dc.type | Article | en_US |
| dc.identifier.doi | https://doi.org/10.1109/TETCI.2021.3136643 | - |
| dc.relation.isPartOf | IEEE Transactions on Emerging Topics in Computational Intelligence | - |
| pubs.issue | 4 | - |
| pubs.publication-status | Published online | - |
| pubs.volume | 7 | - |
| dc.identifier.eissn | 2471-285X | - |
| dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
| Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2021 Institute of Electrical and Electronics Engineers (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 by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | 2.54 MB | Adobe PDF | View/Open |
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