The paper reports that a GCN+GRU model achieves MAE 2.01, RMSE 4.12, and R2 0.956 on Abilene network traffic, outperforming four baselines.
A novel method for improved network traffic prediction using enhanced deep reinforcement learning algorithm,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation
The paper reports that a GCN+GRU model achieves MAE 2.01, RMSE 4.12, and R2 0.956 on Abilene network traffic, outperforming four baselines.