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An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence

Yuan, Bo; Panneerselvam, John; Liu, Lu; Antonopoulos, Nick; Lu, Yao

Authors

Bo Yuan

John Panneerselvam

Lu Liu

Profile image of Nick Antonopoulos

Prof Nick Antonopoulos N.Antonopoulos@napier.ac.uk
Deputy Vice Chancellor and Vice Principal of Research & Innovation

Yao Lu



Abstract

Real-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.

Citation

Yuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., & Lu, Y. (2019). An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4295-4305. https://doi.org/10.1109/tii.2019.2902877

Journal Article Type Article
Acceptance Date Feb 24, 2019
Online Publication Date Mar 4, 2019
Publication Date 2019-07
Deposit Date Dec 10, 2019
Journal IEEE Transactions on Industrial Informatics
Print ISSN 1551-3203
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 15
Issue 7
Pages 4295-4305
DOI https://doi.org/10.1109/tii.2019.2902877
Keywords Control and Systems Engineering; Electrical and Electronic Engineering; Information Systems; Computer Science Applications
Public URL http://researchrepository.napier.ac.uk/Output/1995495