GNN Based Service

Graph Neural Network (GNN)-based services leverage the power of GNNs to process complex, non-Euclidean data, aiming to improve efficiency and accuracy in various applications. Current research focuses on optimizing GNN training and inference, including developing novel architectures like windowed vision GNNs for image processing and efficient sampling-based training methods for large-scale graphs, as well as addressing challenges like memory limitations and imbalanced data. These advancements are significantly impacting fields such as computer vision, sign language recognition, and IoT services by enabling faster, more accurate, and scalable solutions for real-world problems.