*PyGAS* is the practical realization of our *<ins>G</ins>NN<ins>A</ins>uto<ins>S</ins>cale* (GAS) framework, which scales arbitrary message-passing GNNs to large graphs, as described in our paper:
Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec: **[GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings]()***(ICML 2021)*
Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec: **[GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings](http://arxiv.org/abs/2106.05609)***(ICML 2021)*
GAS prunes entire sub-trees of the computation graph by utilizing historical embeddings from prior training iterations, leading to constant GPU memory consumption in respect to input mini-batch size, and maximally expressivity.
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@@ -87,7 +87,7 @@ We use [**Hydra**](https://hydra.cc/) to manage hyperparameter configurations.
## Cite
Please cite [our paper]() if you use this code in your own work:
Please cite [our paper](http://arxiv.org/abs/2106.05609) if you use this code in your own work:
