Dynamic GNN

Dynamic Graph Neural Networks (GNNs) extend the capabilities of traditional GNNs by incorporating temporal information to model evolving graph structures and their associated attributes. Current research emphasizes developing efficient training frameworks, such as pipelined and parallel approaches, to handle the computational demands of processing dynamic graph streams and improving model explainability. These advancements are crucial for diverse applications, including improved seizure detection in EEG data, accelerated materials discovery, and more accurate predictions in domains with constantly changing relationships, such as social networks or biological systems.