description

README.md

@@ -4,10 +4,18 @@
 
 --------------------------------------------------------------------------------
 
+*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.
+*PyGAS* allows for training and inference of GNNs with a constant GPU memory footprint, while it does not drop any input data in comparison to related scalability approaches.
+Our approach based on historical node embeddings is provably able to keep the existing expressiveness properties of the underlying message passing implementation.
+
+*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:
+
 ```python
 from torch_geometric.nn import GCNConv
 from torch_geometric_autoscale import ScalableGNN
 
+
 class GNN(ScalableGNN):
     def __init__(self, num_nodes, in_channels, hidden_channels, out_channels, num_layers):
         super(GNN, self).__init__(num_nodes, hidden_channels, num_layers)
@@ -18,9 +26,13 @@ class GNN(ScalableGNN):
             self.convs.append(GCNConv(hidden_channels, hidden_channels))
         self.convs.append(GCNConv(hidden_channels, out_channels))
 
-    def forward(self, x, adj, n_id):
+    def forward(self, x, adj, batch_size, n_id):
         for conv, history in zip(self.convs[:-1], self.histories):
-            x = conv(x, adj).relu()
-            x = self.push_and_pull(history, x, n_id)
+            x = conv(x, adj).relu_()
+            x = self.push_and_pull(history, x, batch_size, n_id)
         return self.convs[-1](x, adj
 ```
+
+## Installation
+
+## Project Structure