Graph Based

Graph-based methods are revolutionizing data analysis by representing complex relationships as networks, enabling the extraction of insights from diverse data types. Current research focuses on developing robust graph neural networks (GNNs), including variations like graph convolutional networks and graph attention networks, to address challenges such as adversarial attacks and data sparsity, and to improve model interpretability and efficiency. These advancements are significantly impacting fields ranging from recommender systems and healthcare to transportation planning and financial forecasting, offering more accurate predictions and deeper understanding of complex systems.