google-deepmind/graph_nets
Build Graph Nets in Tensorflow
Provides modular abstractions for graph neural networks with separate learnable functions for edge, node, and global-level attribute updates, integrated with Sonnet for flexible model composition. Supports iterative message-passing refinement across graph structure, enabling tasks like shortest-path reasoning, sorting, and physics prediction through a unified architecture. Compatible with TensorFlow 1.x and 2.x across CPU/GPU backends.
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Language
Python
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Apache-2.0
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Last pushed
Dec 12, 2022
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