Temporal Graph Generative Models

Graph Neural Networks (GNNs) have recently emerged as popular methods for learning representations of non-euclidean data often encountered in diverse areas ranging from chemistry to source code generation. Recently, researchers have focused on learning about temporal graphs, wherein the nodes and edges of a graph and their respective features may change over time. In this paper, we focus on a nascent domain: learning generative models on temporal graphs. We have noticed that papers on this topic so far have lacked a standard evaluation for all existing models on the same benchmark of datasets and a solid evaluation protocol. We present extensive comparative experiments on state-of-the-art models from the literature. Furthermore, we propose a rigorous evaluation protocol to assess temporal generation quality, utility, and privacy.