Graph Classification On Peptides Func

Metrics

Results

Performance results of various models on this benchmark

Model Name	AP	Paper Title	Repository
GRED+LapPE	0.7133±0.0011	Recurrent Distance Filtering for Graph Representation Learning
CKGCN	0.6952	CKGConv: General Graph Convolution with Continuous Kernels
GCN+	0.7261 ± 0.0067	Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
GCN	0.5930±0.0023	Long Range Graph Benchmark
Graph Diffuser	0.6651±0.0010	Diffusing Graph Attention	-
DRew-GCN+LapPE	0.7150±0.0044	DRew: Dynamically Rewired Message Passing with Delay
GIN	0.6043±0.0216	How Powerful are Graph Neural Networks?
GRED	0.7085±0.0027	Recurrent Distance Filtering for Graph Representation Learning
GatedGCN-HSG	0.6866±0.0038	Next Level Message-Passing with Hierarchical Support Graphs	-
Exphormer	0.6527±0.0043	Exphormer: Sparse Transformers for Graphs
GatedGCN-tuned	0.6765±0.0047	Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
ESA (Edge set attention, no positional encodings, not tuned)	0.6863±0.0044	An end-to-end attention-based approach for learning on graphs	-
GatedGCN+RWSE+virtual node	0.6685±0.0062	On the Connection Between MPNN and Graph Transformer
GINE	0.5498±0.0079	Long Range Graph Benchmark
GCN-tuned	0.6860±0.0050	Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark
ESA (Edge set attention, no positional encodings, tuned)	0.7071±0.0015	An end-to-end attention-based approach for learning on graphs	-
EIGENFORMER	0.6414	Graph Transformers without Positional Encodings	-
GatedGCN	0.5864±0.0077	Long Range Graph Benchmark
Transformer+LapPE	0.6326±0.0126	Long Range Graph Benchmark
Graph ViT	0.6942±0.0075	A Generalization of ViT/MLP-Mixer to Graphs

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