Conditional Variational Time-Series Graph Auto-Encoder

Network representation learning (also called graph embedding) is the basis for graph tasks such as link prediction, node classification, community discovery, and graph visualization. Most of the existing graph embedding algorithms are mainly developed for static graphs, which is difficult to capture the dynamic characteristics of the real-world networks that evolve over time. At present, research on dynamic network representation learning is still inadequate. This paper proposes a conditional variational time-series graph auto-encoder (TS-CVGAE), which can simultaneously learn the local structure and evolution pattern of a dynamic network. The model improves the traditional graph convolution to obtain time-series graph convolution and uses it to encode the network in the framework of conditional variational auto-encoder. After training, the middle layer of TS-CVGAE is the final network embedding. Experimental results show that the method performs better in link prediction task than the related static and dynamic network representation learning methods with all four real dynamic network datasets.