Layer-wise training for self-supervised learning on graphs
Oscar Pina Verónica Vilaplana
End-to-end training of graph neural networks (GNN) on large graphs presents
several memory and computational challenges, and limits the application to
shallow architectures as depth exponentially increases the memory and space
complexities. In this manuscript, we propose Layer-wise Regularized Graph
Infomax, an algorithm to train GNNs layer by layer in a self-supervised manner.
We decouple the feature propagation and feature transformation carried out by
GNNs to learn node representations in order to derive a loss function based on
the prediction of future inputs. We evaluate the algorithm in inductive large
graphs and show similar performance to other end to end methods and a
substantially increased efficiency, which enables the training of more
sophisticated models in one single device. We also show that our algorithm
avoids the oversmoothing of the representations, another common challenge of
deep GNNs.