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Open access
Aug 2026
SGS-GNN: A Supervised Graph Sparsifier for Graph Neural Networks
SGS-GNN improves F1-scores by 4% relative to full training and up to 30% on heterophilic graphs and outperforms state-of-the-art methods by 4–7% at similar sparsity levels while reducing peak memory usage by up to 3.9×.
Siddhartha Shankar Das, Naheed Anjum Arafat, Muftiqur Rahman et al.
· Proceedings of the 32nd ACM... · 0 citations