Abstract

Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges for ML because traditional static graph methods fail to account for event timings naturally. Newer approaches, such as graph recurrent neural networks (GRNNs), are inherently time-aware and offer advantages over static methods for CTDGs. Yet, GRNNs face another issue: the short truncation of backpropagation-through-time (BPTT) whose impact has never been properly examined until now. In this work, we demonstrate that this truncation can limit the learning of dependencies more than a hop away, resulting in reduced performance. Through experiments on a novel synthetic task as well as real-world datasets, we reveal that there exists a performance gap between full backpropagation-through-time (F-BPTT) and the truncated backpropagation-through-time (T-BPTT) commonly used to train GRNN models. We term this gap the "truncation gap" and argue that understanding and addressing it is essential as the importance of CTDGs grows, discussing potential future directions of research for this type of models.