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cs.LG2023
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 dept…
cs.LG2023
Feature propagation as self-supervision signals on graphs
Oscar Pina, Verónica Vilaplana
Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs. Current algorithms…