Graph Nonvolutional Network

Graph Neural Networks (GNNs) are a type of deep learning model specifically designed to handle graph-structured data. GNNs achieve modeling of complex relationships in graphs through mechanisms of information passing and aggregation at the node, edge, and graph levels, aiming to capture dependencies and feature representations within the graph structure. The primary goal is to enhance the representation learning capabilities of graph data, thereby achieving better performance in tasks such as graph classification, node classification, and link prediction. GNNs have demonstrated significant application value in areas like social network analysis, recommendation systems, and chemical molecular structure prediction.