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 mini-batch size, and maximally expressivity.
As a result, our approach is provably able to maintain the expressive power of the original GNN.
*PyGAS* is implemented in [PyTorch](https://pytorch.org/) and utilizes the [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric)(PyG) library.
*PyGAS* is implemented in [PyTorch](https://pytorch.org/) and utilizes the [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric)(PyG) library.
It provides an easy-to-use interface to convert common and custom GNNs from PyG into its scalable variant:
It provides an easy-to-use interface to convert a common and custom GNN from PyG into its scalable variant:
