Before revision:  From a practical point of view, augmenting existing GNN layers with LRGA produces state of the art results on most datasets in a GNN standard benchmark .
After revision:  From a practical point of view, augmenting existing GNN layers with LRGA produces state of the art results in current GNN benchmarks. Lastly, we observe that augmenting various GNN architectures with LRGA often closes the performance gap between different models .