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SOTA
Graph Classification
Graph Classification On Peptides Func
Graph Classification On Peptides Func
Metrics
AP
Results
Performance results of various models on this benchmark
Columns
Model Name
AP
Paper Title
ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set)
0.7479
An end-to-end attention-based approach for learning on graphs
ECFP + LightGBM
0.7460
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
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
TT + LightGBM
0.7318
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
S²GCN
0.7311±0.0066
Spatio-Spectral Graph Neural Networks
RDKit + LightGBM
0.7311
Molecular Fingerprints Are Strong Models for Peptide Function Prediction
GCN+
0.7261 ± 0.0067
Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence
GraphGPS + HDSE
0.7156±0.0058
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding
DRew-GCN+LapPE
0.7150±0.0044
DRew: Dynamically Rewired Message Passing with Delay
GRED+LapPE
0.7133±0.0011
Recurrent Distance Filtering for Graph Representation Learning
NeuralWalker
0.7096 ± 0.0078
Learning Long Range Dependencies on Graphs via Random Walks
GRED
0.7085±0.0027
Recurrent Distance Filtering for Graph Representation Learning
ESA (Edge set attention, no positional encodings, tuned)
0.7071±0.0015
An end-to-end attention-based approach for learning on graphs
GRIT
0.6988±0.0082
Graph Inductive Biases in Transformers without Message Passing
CKGCN
0.6952
CKGConv: General Graph Convolution with Continuous Kernels
Graph ViT
0.6942±0.0075
A Generalization of ViT/MLP-Mixer to Graphs
GraphMLPMixer
0.6921±0.0054
A Generalization of ViT/MLP-Mixer to Graphs
GatedGCN-HSG
0.6866±0.0038
Next Level Message-Passing with Hierarchical Support Graphs
ESA (Edge set attention, no positional encodings, not tuned)
0.6863±0.0044
An end-to-end attention-based approach for learning on graphs
GCN-tuned
0.6860±0.0050
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
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Graph Classification On Peptides Func | SOTA | HyperAI