Graph Neural Network

Graph Neural Networks (GNNs) are a type of deep learning model specifically designed to handle graph-structured data. GNNs aim to capture complex dependencies and structural information in graphs through node-level, edge-level, and graph-level representation learning. Their core objective is to achieve efficient embeddings for each node in the graph by iteratively aggregating information from neighboring nodes. GNNs have demonstrated significant application value in areas such as social network analysis, chemical molecular structure prediction, recommendation systems, and knowledge graphs, effectively addressing the challenges posed by non-Euclidean data that traditional methods struggle to handle.