Graphical Neural Network

Graphical neural networks (GNNs) are a class of deep learning models designed to analyze and learn from data represented as graphs, capturing complex relationships between interconnected nodes. Current research focuses on applying GNNs to diverse problems, including multi-agent navigation, time-series forecasting, and classification tasks involving 3D data, often incorporating techniques like federated learning and reinforcement learning to enhance model performance and data efficiency. This versatility makes GNNs a powerful tool with significant impact across various scientific domains and practical applications, offering improved accuracy and efficiency in tasks ranging from autonomous systems to climate modeling.