graph-neural-network

Graph Neural Networks (GNNs) are a class of neural networks that operate on graph data structures, enabling them to capture the relationships and interactions between nodes. They are particularly effective in tasks where relationships are key, such as social network analysis and molecular chemistry. A common misconception is that GNNs are merely a variant of traditional neural networks; in reality, they incorporate message passing and neighborhood aggregation techniques to function effectively on non-Euclidean data.