GNN Method

Graph Neural Networks (GNNs) are a class of deep learning models designed to process graph-structured data, aiming to learn representations that capture both node features and graph topology. Current research focuses on improving GNN efficiency and scalability through distributed training algorithms, addressing limitations like oversmoothing and the computational cost of large graphs, and enhancing model explainability and fairness. These advancements are crucial for broadening the applicability of GNNs across diverse fields, including drug discovery, social network analysis, and traffic prediction, where their ability to model complex relationships offers significant advantages.