Mechanism failed
2026
Two-level local/coarse GNN training
Partition a large graph into induced subgraphs and perform most parameter updates using only local subgraphs, interleaving them with inexpensive global updates on a randomly subsampled coarse graph. The coarse correction preserves information about cross-partition dependencies while reducing full-graph message passing and communication cost.
Paper: Two-level domain-decomposition AdaGrad method for scalable training of graph neural networks
arXiv:2608.22575