Layer-Wise Training for Self-Supervised Learning on Graphs
Layer-Wise Training for Self-Supervised Learning on Graphs
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 …