A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network
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-c...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870254/ |
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| author | Yulian Li Yang Su |
| author_facet | Yulian Li Yang Su |
| author_sort | Yulian Li |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0ebe8b1ba17244c3ae043903c62a224f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0ebe8b1ba17244c3ae043903c62a224f2025-02-12T00:01:23ZengIEEEIEEE Access2169-35362025-01-0113243982441010.1109/ACCESS.2025.353826510870254A Network Traffic Prediction Model Based on Layered Training Graph Convolutional NetworkYulian Li0https://orcid.org/0009-0008-3240-0259Yang Su1https://orcid.org/0009-0002-9175-7756School of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, Shaanxi, ChinaSchool of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an, Shaanxi, ChinaRouting 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.https://ieeexplore.ieee.org/document/10870254/Gated recurrent unitgraph convolutional networkspace-correlated featurestime-correlated featurestraffic prediction |
| spellingShingle | Yulian Li Yang Su A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network IEEE Access Gated recurrent unit graph convolutional network space-correlated features time-correlated features traffic prediction |
| title | A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network |
| title_full | A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network |
| title_fullStr | A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network |
| title_full_unstemmed | A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network |
| title_short | A Network Traffic Prediction Model Based on Layered Training Graph Convolutional Network |
| title_sort | network traffic prediction model based on layered training graph convolutional network |
| topic | Gated recurrent unit graph convolutional network space-correlated features time-correlated features traffic prediction |
| url | https://ieeexplore.ieee.org/document/10870254/ |
| work_keys_str_mv | AT yulianli anetworktrafficpredictionmodelbasedonlayeredtraininggraphconvolutionalnetwork AT yangsu anetworktrafficpredictionmodelbasedonlayeredtraininggraphconvolutionalnetwork AT yulianli networktrafficpredictionmodelbasedonlayeredtraininggraphconvolutionalnetwork AT yangsu networktrafficpredictionmodelbasedonlayeredtraininggraphconvolutionalnetwork |