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Graph Classification On Peptides Func

المقاييس

AP

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
AP
Paper TitleRepository
GRED+LapPE0.7133±0.0011Recurrent Distance Filtering for Graph Representation Learning-
CKGCN0.6952CKGConv: General Graph Convolution with Continuous Kernels-
GCN+0.7261 ± 0.0067Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence-
GCN0.5930±0.0023Long Range Graph Benchmark-
Graph Diffuser0.6651±0.0010Diffusing Graph Attention-
DRew-GCN+LapPE0.7150±0.0044DRew: Dynamically Rewired Message Passing with Delay-
GIN0.6043±0.0216How Powerful are Graph Neural Networks?-
GRED0.7085±0.0027Recurrent Distance Filtering for Graph Representation Learning-
GatedGCN-HSG0.6866±0.0038Next Level Message-Passing with Hierarchical Support Graphs-
Exphormer0.6527±0.0043Exphormer: Sparse Transformers for Graphs-
GatedGCN-tuned0.6765±0.0047Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark-
ESA (Edge set attention, no positional encodings, not tuned)0.6863±0.0044An end-to-end attention-based approach for learning on graphs-
GatedGCN+RWSE+virtual node0.6685±0.0062On the Connection Between MPNN and Graph Transformer-
GINE0.5498±0.0079Long Range Graph Benchmark-
GCN-tuned0.6860±0.0050Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark-
ESA (Edge set attention, no positional encodings, tuned)0.7071±0.0015An end-to-end attention-based approach for learning on graphs-
EIGENFORMER0.6414Graph Transformers without Positional Encodings-
GatedGCN0.5864±0.0077Long Range Graph Benchmark-
Transformer+LapPE0.6326±0.0126Long Range Graph Benchmark-
Graph ViT0.6942±0.0075A Generalization of ViT/MLP-Mixer to Graphs-
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