Graph Neural Networks
Graph Neural Networks (GNNs) are a class of neural networks designed to operate directly on graph-structured data, enabling machine learning models to capture dependencies and relationships between entities. They process nodes, edges, and global graph attributes by aggregating information from neighboring nodes, making them effective for tasks like node classification, link prediction, and graph classification. GNNs extend traditional deep learning to non-Euclidean data, such as social networks, molecular structures, and recommendation systems.
Developers should learn GNNs when working with relational or interconnected data where traditional neural networks (like CNNs or RNNs) fall short, such as in social network analysis, drug discovery, fraud detection, or knowledge graphs. They are essential for applications requiring understanding of complex relationships, as they can model dependencies that are not captured by sequential or grid-based data structures, improving accuracy in tasks like community detection or protein interaction prediction.