基于动态扩散卷积交互图神经网络的网络流量预测.

The existing network traffic prediction models have problems such as weak generalization ability and low prediction accuracy. To solve this problem, This paper point out a prediction model combining dynamic diffusion convolution module and convolution interaction module. The dynamic diffusion convolution module can extract the complex spatial and dynamic characteristics of network traffic, while the convolution interaction module can capture the temporal characteristics of the traffic. The organic combination of the two can effectively predict the traffic in the network. This paper verifies the effectiveness of the proposed dynamic diffusion convolutional interaction graph neural network model (DDCIGNN) by comparative experiments with other network traffic prediction models on the flow data of the US Energy Science Network (ESnet). Experimental results show that the root mean square error (RMSE) of the proposed model is optimized by about 13.0% in the best case, which indicates that the model can perform better in network traffic prediction. [ABSTRACT FROM AUTHOR]