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SOTA
Graphenklassifikation
Graph Classification On Peptides Func
Graph Classification On Peptides Func
Metriken
AP
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
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