An inductive content-augmented network embedding model for edge artificial intelligence
Affiliation
University of DerbyTongji University, Shanghai, China
University of Leicester
Edinburgh Napier University
Issue Date
2019-03-04
Metadata
Show full item recordAbstract
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., 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.Publisher
IEEEJournal
IEEE Transactions on Industrial InformaticsDOI
10.1109/TII.2019.2902877Additional Links
https://ieeexplore.ieee.org/document/8658146/authorsType
ArticleLanguage
enISSN
15513203EISSN
19410050ae974a485f413a2113503eed53cd6c53
10.1109/TII.2019.2902877
Scopus Count
Collections
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International