paper-with-me

Graph Classification 벤치마크

Graph Classification on Peptides-func

88개 결과 · ⬇ CSV · JSON

AP

0.5498 0.5993 0.6488 0.6984 0.7479 2018-10 2026-11 GIN — 0.6043 (2018-10-01) GIN — 0.6043 (2018-10-01) GCNII — 0.5543 (2020-07-04) GCNII — 0.5543 (2020-07-04) GPS — 0.6535 (2022-05-25) GPS — 0.6535 (2022-05-25) SAN+RWSE — 0.6439 (2022-06-16) SAN+LapPE — 0.6384 (2022-06-16) Transformer+LapPE — 0.6326 (2022-06-16) GatedGCN+RWSE — 0.6069 (2022-06-16) GCN — 0.593 (2022-06-16) GatedGCN — 0.5864 (2022-06-16) GINE — 0.5498 (2022-06-16) SAN+RWSE — 0.6439 (2022-06-16) SAN+LapPE — 0.6384 (2022-06-16) Transformer+LapPE — 0.6326 (2022-06-16) GatedGCN+RWSE — 0.6069 (2022-06-16) GCN — 0.593 (2022-06-16) GatedGCN — 0.5864 (2022-06-16) GINE — 0.5498 (2022-06-16) Graph ViT — 0.6942 (2022-12-27) GraphMLPMixer — 0.6921 (2022-12-27) Graph ViT — 0.6942 (2022-12-27) GraphMLPMixer — 0.6921 (2022-12-27) GatedGCN+RWSE+virtual node — 0.6685 (2023-01-27) GatedGCN+RWSE+virtual node — 0.6685 (2023-01-27) MGT+WavePE — 0.6817 (2023-02-17) MGT+WavePE — 0.6817 (2023-02-17) Graph Diffuser — 0.6651 (2023-03-01) Graph Diffuser — 0.6651 (2023-03-01) Exphormer — 0.6527 (2023-03-10) Exphormer — 0.6527 (2023-03-10) DRew-GCN+LapPE — 0.715 (2023-05-13) DRew-GCN+LapPE — 0.715 (2023-05-13) GRIT — 0.6988 (2023-05-27) GRIT — 0.6988 (2023-05-27) ViT-PS — 0.6575 (2023-06-05) ViT-PS — 0.6575 (2023-06-05) CIN++-500k — 0.6569 (2023-06-06) CIN++-500k — 0.6569 (2023-06-06) PathNN — 0.6816 (2023-06-09) PathNN — 0.6816 (2023-06-09) GraphGPS + HDSE — 0.7156 (2023-08-22) GraphGPS + HDSE — 0.7156 (2023-08-22) GCN-tuned — 0.686 (2023-09-01) GatedGCN-tuned — 0.6765 (2023-09-01) GINE-tuned — 0.6621 (2023-09-01) GPS-tuned — 0.6534 (2023-09-01) GCN-tuned — 0.686 (2023-09-01) GatedGCN-tuned — 0.6765 (2023-09-01) GINE-tuned — 0.6621 (2023-09-01) GPS-tuned — 0.6534 (2023-09-01) GRED+LapPE — 0.7133 (2023-12-03) GRED — 0.7085 (2023-12-03) GRED+LapPE — 0.7133 (2023-12-03) GRED — 0.7085 (2023-12-03) EIGENFORMER — 0.6414 (2024-01-31) EIGENFORMER — 0.6414 (2024-01-31) TIGT — 0.6679 (2024-02-03) TIGT — 0.6679 (2024-02-03) ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set) — 0.7479 (2024-02-16) ESA + RWSE (Edge set attention, Random Walk Structural Encoding, tuned) — 0.7357 (2024-02-16) ESA (Edge set attention, no positional encodings, tuned) — 0.7071 (2024-02-16) ESA (Edge set attention, no positional encodings, not tuned) — 0.6863 (2024-02-16) ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set) — 0.7479 (2024-02-16) ESA + RWSE (Edge set attention, Random Walk Structural Encoding, tuned) — 0.7357 (2024-02-16) ESA (Edge set attention, no positional encodings, tuned) — 0.7071 (2024-02-16) ESA (Edge set attention, no positional encodings, not tuned) — 0.6863 (2024-02-16) CKGCN — 0.6952 (2024-04-21) CKGCN — 0.6952 (2024-04-21) S²GCN — 0.7311 (2024-05-29) S²GCN — 0.7311 (2024-05-29) NeuralWalker — 0.7096 (2024-06-05) NeuralWalker — 0.7096 (2024-06-05) GCN + PANDA — 0.6028 (2024-06-06) GCN + PANDA — 0.6028 (2024-06-06) GatedGCN-HSG — 0.6866 (2024-06-22) GatedGCN-HSG — 0.6866 (2024-06-22) MOLTOP — 0.6459 (2024-10-17) MOLTOP — 0.6459 (2024-10-17) ECFP + LightGBM — 0.746 (2025-01-29) TT + LightGBM — 0.7318 (2025-01-29) RDKit + LightGBM — 0.7311 (2025-01-29) ECFP + LightGBM — 0.746 (2025-01-29) TT + LightGBM — 0.7318 (2025-01-29) RDKit + LightGBM — 0.7311 (2025-01-29) GCN+ — 0.7261 (2025-02-13) GCN+ — 0.7261 (2025-02-13) GIN — 0.6043 (2018-10-01) GPS — 0.6535 (2022-05-25) Graph ViT — 0.6942 (2022-12-27) DRew-GCN+LapPE — 0.715 (2023-05-13) GraphGPS + HDSE — 0.7156 (2023-08-22) ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set) — 0.7479 (2024-02-16)
RankModel AP Extra Training Data PaperCodeYear
1 ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set) 0.7479 ✓ An end-to-end attention-based approach for learning on graphs davidbuterez/edge-set-attention 2024
2 ECFP + LightGBM 0.7460 Molecular Fingerprints Are Strong Models for Peptide Function Prediction arch4ngel21/scikit-fingerprints · scikit-fingerprints/scikit-fingerprints · scikit-fingerprints/peptides_molecular_fingerprints_classification · +1 2025
3 ESA + RWSE (Edge set attention, Random Walk Structural Encoding, tuned) 0.7357±0.0036 An end-to-end attention-based approach for learning on graphs davidbuterez/edge-set-attention 2024
4 TT + LightGBM 0.7318 Molecular Fingerprints Are Strong Models for Peptide Function Prediction arch4ngel21/scikit-fingerprints · scikit-fingerprints/scikit-fingerprints · scikit-fingerprints/peptides_molecular_fingerprints_classification · +1 2025
5 S²GCN 0.7311±0.0066 Spatio-Spectral Graph Neural Networks sigeisler/s2gnn 2024
6 RDKit + LightGBM 0.7311 Molecular Fingerprints Are Strong Models for Peptide Function Prediction arch4ngel21/scikit-fingerprints · scikit-fingerprints/scikit-fingerprints · scikit-fingerprints/peptides_molecular_fingerprints_classification · +1 2025
7 GCN+ 0.7261 ± 0.0067 Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence LUOyk1999/GNNPlus 2025
8 GraphGPS + HDSE 0.7156±0.0058 Enhancing Graph Transformers with Hierarchical Distance Structural Encoding luoyk1999/hdse 2023
9 DRew-GCN+LapPE 0.7150±0.0044 DRew: Dynamically Rewired Message Passing with Delay bengutteridge/drew 2023
10 GRED+LapPE 0.7133±0.0011 Recurrent Distance Filtering for Graph Representation Learning skeletondyh/gred 2023
11 NeuralWalker 0.7096 ± 0.0078 Learning Long Range Dependencies on Graphs via Random Walks borgwardtlab/neuralwalker 2024
12 GRED 0.7085±0.0027 Recurrent Distance Filtering for Graph Representation Learning skeletondyh/gred 2023
13 ESA (Edge set attention, no positional encodings, tuned) 0.7071±0.0015 An end-to-end attention-based approach for learning on graphs davidbuterez/edge-set-attention 2024
14 GRIT 0.6988±0.0082 Graph Inductive Biases in Transformers without Message Passing liamma/grit · linusbao/MoSE 2023
15 CKGCN 0.6952 CKGConv: General Graph Convolution with Continuous Kernels networkslab/ckgconv 2024
16 Graph ViT 0.6942±0.0075 A Generalization of ViT/MLP-Mixer to Graphs XiaoxinHe/Graph-ViT-MLPMixer · asarigun/GraphMixerNetworks · 2023-MindSpore-1/ms-code-20 2022
17 GraphMLPMixer 0.6921±0.0054 A Generalization of ViT/MLP-Mixer to Graphs XiaoxinHe/Graph-ViT-MLPMixer · asarigun/GraphMixerNetworks · 2023-MindSpore-1/ms-code-20 2022
18 GatedGCN-HSG 0.6866±0.0038 Next Level Message-Passing with Hierarchical Support Graphs carlosinator/support-graphs 2024
19 ESA (Edge set attention, no positional encodings, not tuned) 0.6863±0.0044 An end-to-end attention-based approach for learning on graphs davidbuterez/edge-set-attention 2024
20 GCN-tuned 0.6860±0.0050 Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark toenshoff/lrgb · Fedzbar/laser-release 2023
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