Analysis of Time Series Graph Data Using Graph Autoencoders

Abstract

Recently, research on graph neural networks has attracted attention. In particular, graph convolution is applied to the analysis of human-to-human interaction in SNS and road traffic network to predict future interaction and traffic volume. However, these graph data are usually time-series graphs. It is difficult to apply analysis methods to static graphs. In this paper, we apply graph autoencoders to time-series graphs.