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Rethinking Graph Lottery Tickets: Graph Sparsity Matters

ICLR 2023 · Graph learning › GNN architectures · approach: Robustness, privacy, and fairness of GNNs · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add the Wasserstein-distance auxiliary loss head to guide edge pruning (ablation)UGS-style GNN sparsification · Cora, Citeseer, and PubMedtest accuracyHelped
Increase the auxiliary-loss weight from 0.1 to 1 (ablation)Proposed GNN sparsification · High graph sparsitytest accuracyHurt
Use min-max adversarial optimization for the graph mask instead of UGS optimization (ablation)GNN sparsification with WD loss (UGS+WD comparison) · High graph sparsitytest accuracyHelped
Increase graph sparsity relative to weight sparsity (ablation)Pruned GNN · Cora, Citeseer, and PubMedtest accuracyHurt
Transfer the post-trained source-task winning ticket rather than a randomly initialized GNN3-layer GCN transferred from arXiv to MAG · MAGtarget-task test accuracyHelped
Transfer the re-initialized source-task winning ticket rather than a randomly initialized GNN3-layer GCN transferred from arXiv to MAG · MAGtarget-task test accuracyHelped
Increase GLT sparsity (ablation)GLT transferred from arXiv to MAG · MAGtransfer ratioHurt
Prune graph edges beyond the low-sparsity regimeUGS · Coratest accuracyHurt
Add graph sparsification to GCN on PubMed, where pruning can remove noisy edgesGCN · PubMedtest accuracyHelped

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