Furthermore, research into dynamic network embedding, which captures temporal evolution in graph structures, presents promising avenues for predictive tasks such as link prediction and anomaly ...

New frameworks like Graph WaveNet, ST-GRAT, StemGNN, and other research-focused efforts gave the collaborators sufficient methodologies to add richer spatiotemporal layers along with dynamic graph ...

With artificial neural networks becoming more popular and capable, GNNs have become a powerful tool for many important applications.

Graph Neural Networks are on the path to becoming more mainstream, with exciting opportunities for the maturation of essential operations across message passing such as scatters, gathers, segmented ...