A Data-Driven Graph Generative Model for Temporal Interaction Networks

Deep graph generative models have recently received a surge of attention due to its superiority of modeling realistic graphs in a variety of domains, including biology, chemistry, and social science. Despite the initial success, most, if not all, of the existing works are designed for static networks. Nonetheless, many realistic networks are intrinsically dynamic and presented as a collection of system logs (i.e., timestamped edges between entities). In this paper, we propose an end-to-end deep generative framework named TagGen, which can directly learn from a series of timestamped nodes and edges and preserve the structural and temporal characteristics of the input data. The experimental results in seven real-world data sets across a variety of metrics demonstrate that (1) TagGen outperforms all baselines in the temporal interaction network generation problem, and (2) TagGen significantly boosts the performance of the prediction models in the tasks of anomaly detection and link prediction.