In recent years, there has been an increasing interest in the use of graph neural networks (GNNs) for analyzing dynamic graphs, which are graphs that evolve over time. However, there is still a lack of understanding of how different temporal graph neural network (TGNs) configurations can impact the accuracy of predictions on dynamic graphs. Moreover, the hunt for benchmark datasets for these TGNs models is still ongoing. Up until recently, Pytorch Geometric Temporal came up with a few benchmark datasets but most of these datasets have not been analyzed with different TGN models to establish the state-of-the-art. Therefore, this project aims to address this gap in the literature by performing a qualitative analysis of spatial-temporal dependence structure learning on dynamic graphs, as well as a comparative study of the effectiveness of selected TGNs on node and edge prediction tasks. Additionally, an extensive ablation study will be conducted on different variants of the best-performing TGN to identify the key factors contributing to its performance. By achieving these objectives, this project will provide valuable insights into the design and optimization of TGNs for dynamic graph analysis, with potential applications in areas such as disease spread prediction, social network analysis, traffic prediction, and more. Moreover, an attempt is made to convert snapshot-based data to the event-based dataset and make it compatible with the SOTA model namely TGN to perform node regression task.

翻译：近年来，人们对使用图神经网络分析动态图（即随时间演化的图）的兴趣日益增长。然而，关于不同时间图神经网络配置如何影响动态图预测准确性的理解仍然不足。此外，针对这些时间图神经网络模型的基准数据集的寻找仍在进行中。直到最近，PyTorch Geometric Temporal提出了一些基准数据集，但这些数据集大多尚未与不同的时间图神经网络模型进行对比分析以确立最新技术水平。因此，本项目旨在通过定性分析动态图上的时空依赖结构学习，以及对选定时间图神经网络在节点和边预测任务上的有效性进行比较研究，来填补这一文献空白。此外，将对表现最佳的时间图神经网络的不同变体进行广泛的消融研究，以识别其性能的关键因素。通过实现这些目标，本项目将为设计并优化用于动态图分析的时间图神经网络提供宝贵见解，潜在应用领域包括疾病传播预测、社交网络分析、交通预测等。此外，我们还尝试将基于快照的数据转换为基于事件的数据集，并使其与最新模型（即时间图神经网络）兼容，以执行节点回归任务。