Bibliographic Details
| Title: |
A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network |
| Authors: |
Yulian Li, Yang Su |
| Source: |
IEEE Access, Vol 13, Pp 24398-24410 (2025) |
| Publisher Information: |
IEEE, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Gated recurrent unit, graph convolutional network, space-correlated features, time-correlated features, traffic prediction, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
Routing deployment and resource scheduling in communication networks require accurate traffic prediction. Neural network-based models that extract the time-correlated or space-correlated features of traffic flow have been developed for traffic prediction. The conventional model that extracts space-correlated features of traffic flow have the problem of high computational complexity and long training time which limits the model’s application on rapid routing deployment. This paper therefore proposes a layered training graph convolutional network (LT-GCN) to decrease the training time greatly with the nearly same prediction accuracy as graph convolutional network (GCN). Instead of training on parameters in all hidden layers simultaneously, LT-GCN develops a new layer-by-layer training pattern for multiple hidden layers to degrade the computational complexity in training process. LT-GCN is then further integrated with gated recurrent unit (GRU) that is called LTGG model to achieve the joint extraction of time-correlated and space-correlated features of traffic flow for more accurate prediction. Experimental results demonstrate that LT-GCN outperforms the classical GCN model on training time and LTGG exhibits greater performance than other benchmark models on prediction accuracy. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10870254/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2025.3538265 |
| Access URL: |
https://doaj.org/article/0ebe8b1ba17244c3ae043903c62a224f |
| Accession Number: |
edsdoj.0ebe8b1ba17244c3ae043903c62a224f |
| Database: |
Directory of Open Access Journals |
