Graph Nets
DeepMind's library for building graph networks in TensorFlow and Sonnet, enabling graph-structured data processing with neural networks.
What is Graph Nets?
Graph Nets is DeepMind's library for building graph networks in TensorFlow and Sonnet. It enables developers and researchers to create neural network models that operate directly on graph-structured data, where inputs and outputs are graphs with updated edge, node, and global attributes. This approach is particularly valuable for problems involving relational reasoning and structured data.
Machine learning researchers and developers working with graph-structured data, particularly those using TensorFlow for problems in relational reasoning, physical simulation, or combinatorial optimization.
Developers choose Graph Nets because it provides a clean, research-backed implementation of graph networks from DeepMind, with production-ready TensorFlow/Sonnet integration and comprehensive examples demonstrating practical applications across different domains.
Overview
Build Graph Nets in Tensorflow
Use Cases
Best For
- Implementing graph neural networks for relational reasoning tasks
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