Graph Neural Network Model

Graph neural networks (GNNs) are a class of deep learning models designed to analyze and learn from graph-structured data, aiming to extract meaningful representations from complex relationships between interconnected entities. Current research focuses on improving GNN efficiency and scalability for large datasets, exploring various architectures like graph convolutional networks (GCNs), graph attention networks (GATs), and graph transformers, and developing techniques to enhance explainability and address challenges like overfitting and the cold start problem. GNNs are proving valuable across diverse fields, including recommendation systems, malware detection, and power system optimization, offering significant potential for advancing data analysis in domains with inherently relational data.