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...
Saved in:
Main Authors: | Yulian Li, Yang Su |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10870254/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A power load forecasting method using cosine similarity and a graph convolutional network
by: JI Shan, et al.
Published: (2025-01-01) -
Graph attention convolution network for power flow calculation considering grid uncertainty
by: Haochen Li, et al.
Published: (2025-04-01) -
A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network
by: Ming Jiang, et al.
Published: (2025-01-01) -
Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network
by: Xiang Yin, et al.
Published: (2025-01-01) -
Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network
by: Guimei Yin, et al.
Published: (2025-02-01)