Dynamic Graph Datasets

Dynamic graph datasets represent evolving relationships between entities over time, posing significant challenges for machine learning. Current research focuses on developing robust and scalable dynamic graph neural networks (DyGNNs) that address issues like distribution shifts, adversarial attacks, and the need for efficient training on large, sparse datasets. These efforts involve exploring novel architectures, such as those incorporating spiking neural networks or information bottleneck principles, and improving training strategies through techniques like graph partitioning and data augmentation. Improved DyGNNs hold significant potential for advancing applications in various fields, including social network analysis, recommendation systems, and anomaly detection.