Temporalizing static graph autoencoders to handle temporal networks

Abstract

Graph autoencoders (GAE), also known as graph embedding methods, learn latent representations of the nodes of a graph in a low-dimensional space where the structural information is preserved. While real-world graphs are generally dynamic, only a few embedding methods handle the temporal dimension: Even though they have proven their reliability, the majority of the embedding techniques address the case of static networks and present poor performances when applied to temporal ones. In this paper, we present a generic method to temporalize static graph autoencoders, i.e. adapt different static graph embedding methods to the case of temporal networks. This is made possible by learning optimal connections between timesteps' graphs in order to form a single merged spatio-temporal network. We prove that this highly improves the inference tasks' accuracy of the temporalized methods. We also show that the learned connections are directly related to nodes characteristics and can be used beyond the scope of the embedding they are designed for.