An inductive content-augmented network embedding model for edge artificial intelligence
Journal article
Yuan, Bo, Panneerselvam, J., Liu, Lu, Antonopoulos, Nick and Lu, Yao 2019. An inductive content-augmented network embedding model for edge artificial intelligence. IEEE Transactions on Industrial Informatics. 15 (7), pp. 4295-4305. https://doi.org/10.1109/TII.2019.2902877
| Authors | Yuan, Bo, Panneerselvam, J., Liu, Lu, Antonopoulos, Nick and Lu, Yao |
|---|---|
| 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. |
| Keywords | Artificial intelligence (AI); deep learning; edge computing; network embedding; resource discovery |
| Year | 2019 |
| Journal | IEEE Transactions on Industrial Informatics |
| Journal citation | 15 (7), pp. 4295-4305 |
| Publisher | IEEE |
| ISSN | 15513203 |
| 19410050 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TII.2019.2902877 |
| Web address (URL) | http://hdl.handle.net/10545/625150 |
| http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| hdl:10545/625150 | |
| Publication dates | 04 Mar 2019 |
| Publication process dates | |
| Deposited | 04 Sep 2020, 10:20 |
| Accepted | 24 Feb 2019 |
| Rights | Attribution-NonCommercial-ShareAlike 4.0 International |
| Contributors | University of Derby, Tongji University, Shanghai, China, University of Leicester and Edinburgh Napier University |
| File | File Access Level Open |
| File | File Access Level Open |
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