*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:
*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]()***(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 node size without dropping any data.
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 node size without dropping any data.
As a result, our approach is provably able to maintain the expressive power of the original GNN.
As a result, our approach is provably able to maintain the expressive power of the original GNN.
