TensorFlow GNN
TensorFlow GNN (Graph Neural Networks) is a library built on TensorFlow for developing and training graph neural network models. It provides scalable, flexible tools to handle graph-structured data, enabling tasks like node classification, link prediction, and graph-level predictions. The library integrates with TensorFlow's ecosystem, supporting both research and production deployments.
Developers should learn TensorFlow GNN when working with data that has relational structures, such as social networks, molecular graphs, or recommendation systems, as it efficiently captures dependencies between entities. It is particularly useful for applications in drug discovery, fraud detection, and knowledge graphs, where traditional neural networks fall short in modeling complex relationships.