Capturing Spatial-Temporal Correlations with Attention Based Graph Convolutional Networks for Network Traffic Prediction

Network traffic prediction is essential and significant to network management and network security. Existing prediction methods cannot well capture the temporal-spatial correlations hidden in the network traffic and suffer from a prediction accuracy degradation for two reasons. First, existing approaches concentrate on capturing the spatial relations and dependence in an explicit network topology graph, which fails to reflect the inherent relations in network traffic. Second, the common recurrent neural network models which are leveraged to learn the temporal relations in network traffic exhibit a poor performance in long-term prediction for its limited receptive field. To tackle these two problems, we propose an Attention-based Graph Convolutional Network model (AGCN) for capturing both the spatial and temporal correlations in network traffic. To catch the hidden spatial dependencies in network traffic, we combine graph attention network with graph convolutional network to mine the spatial relationships of network traffic. To efficiently learn the temporal long-term relations embedded in network traffic, we design a dilated convolution module to enable an exponentially growing receptive field for handling long sequences. Experimental results on three network traffic datasets show that AGCN has excellent performance in terms of prediction accuracy and inference time compared to current mainstream methods.