GNN Based

Graph neural networks (GNNs) are revolutionizing data analysis by leveraging the power of graph structures to model relationships between data points. Current research focuses on improving GNN architectures, such as incorporating transformers for enhanced temporal modeling and long-range dependencies, and optimizing their performance through techniques like low-rank kernel models and hardware acceleration. These advancements are significantly impacting diverse fields, including recommender systems, computer vision, and time series forecasting, by enabling more accurate, efficient, and explainable models for complex data.