Network Link Status Classification Method Based on Graph Autoencoder

Abstract

AbstractIn recent years, with the rapid development of the Internet, the amount of data in the network increases exponentially, and people have higher requirements on the quality of network links, which poses new challenges to network management and analysis. Network link status classification can be used to predict network link status categories by using the current network topology information and feature information, facilitating network management and analysis. However, most of the existing models consider to predict the time sequence information of the link status without considering the topology information and node attributes of the network. Therefore, we design a network link status classification model based on graph autoencoder for computer network scenarios. The attention mechanism is introduced into the encoder, and then the node vector is spliced into edge vector, which is then input into the multi-layer perceptron for classification. Finally, the feasibility and validity of the link status classification model are verified on two datasets.KeywordsLink predictionGraph autoencoderMultilayer perceptronNode embedding