Capturing spatial–temporal correlations with Attention based Graph Convolutional Network for network traffic prediction

Abstract

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, 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. Second, the existing methods for modeling the spatial relationship of network traffic focus on capturing the distance relationship between nodes in the network topology, which cannot reflect the implicit correlation between network nodes. 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.