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Graph Classification 벤치마크

Graph Classification on Mutagenicity

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Accuracy

78.1 79.32 80.55 81.78 83 2019-10 2026-11 NDP — 78.1 (2019-10-24) NDP — 78.1 (2019-10-24) HGP-SL — 82.15 (2019-11-14) HGP-SL — 82.15 (2019-11-14) MEWISPool — 80.66 (2021-07-03) MEWISPool — 80.66 (2021-07-03) TREE-G — 83.0 (2022-07-06) TREE-G — 83.0 (2022-07-06) S-CGIB — 81.12 (2025-02-20) S-CGIB — 81.12 (2025-02-20) NDP — 78.1 (2019-10-24) HGP-SL — 82.15 (2019-11-14) TREE-G — 83.0 (2022-07-06)
RankModel Accuracy PaperCodeYear
1 TREE-G 83 TREE-G: Decision Trees Contesting Graph Neural Networks mayabechlerspeicher/tree-g 2022
2 HGP-SL 82.15 Hierarchical Graph Pooling with Structure Learning dmlc/dgl · cszhangzhen/HGP-SL · gsl-benchmark/gslb 2019
3 S-CGIB 81.12±0.90 Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck NSLab-CUK/S-CGIB 2025
4 MEWISPool 80.66 Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks mewispool/mewispool 2021
5 NDP 78.1 Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling danielegrattarola/decimation-pooling 2019
6 TREE-G 83 TREE-G: Decision Trees Contesting Graph Neural Networks mayabechlerspeicher/tree-g 2022
7 HGP-SL 82.15 Hierarchical Graph Pooling with Structure Learning dmlc/dgl · cszhangzhen/HGP-SL · gsl-benchmark/gslb 2019
8 S-CGIB 81.12±0.90 Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck NSLab-CUK/S-CGIB 2025
9 MEWISPool 80.66 Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks mewispool/mewispool 2021
10 NDP 78.1 Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling danielegrattarola/decimation-pooling 2019
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