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dc.contributor.authorYuan, Bo
dc.contributor.authorPanneerselvam, John
dc.contributor.authorLiu, Lu
dc.contributor.authorAntonopoulos, Nick
dc.contributor.authorLu, Yao
dc.date.accessioned2020-09-04T10:20:52Z
dc.date.available2020-09-04T10:20:52Z
dc.date.issued2019-03-04
dc.identifier.citationYuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., and Lu, Y. (2019). ‘An inductive content-augmented network embedding model for edge artificial intelligence’. IEEE Transactions on Industrial Informatics, 15(7), pp. 1-11.en_US
dc.identifier.issn15513203
dc.identifier.doi10.1109/TII.2019.2902877
dc.identifier.urihttp://hdl.handle.net/10545/625150
dc.description.abstractReal-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.en_US
dc.description.sponsorshipNatural Science Foundation of Jiangsu Provinceen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/8658146/authorsen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectArtificial intelligence (AI)en_US
dc.subjectdeep learningen_US
dc.subjectedge computingen_US
dc.subjectnetwork embeddingen_US
dc.subjectresource discoveryen_US
dc.titleAn inductive content-augmented network embedding model for edge artificial intelligenceen_US
dc.typeArticleen_US
dc.identifier.eissn19410050
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentTongji University, Shanghai, Chinaen_US
dc.contributor.departmentUniversity of Leicesteren_US
dc.contributor.departmentEdinburgh Napier Universityen_US
dc.identifier.journalIEEE Transactions on Industrial Informaticsen_US
dc.identifier.eid2-s2.0-85066307408
dc.identifier.scopusidSCOPUS_ID:85066307408
dc.source.journaltitleIEEE Transactions on Industrial Informatics
dc.source.volume15
dc.source.issue7
dc.source.beginpage4295
dc.source.endpage4305
dcterms.dateAccepted2019-02-24
dc.author.detailSTF1867en_US


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International