Graph Classification On Peptides Func

المقاييس

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

جدول المقارنة

اسم النموذج	AP
recurrent-distance-encoding-neural-networks	0.7133±0.0011
ckgconv-general-graph-convolution-with	0.6952
unlocking-the-potential-of-classic-gnns-for	0.7261 ± 0.0067
long-range-graph-benchmark	0.5930±0.0023
diffusing-graph-attention	0.6651±0.0010
drew-dynamically-rewired-message-passing-with	0.7150±0.0044
how-powerful-are-graph-neural-networks	0.6043±0.0216
recurrent-distance-encoding-neural-networks	0.7085±0.0027
next-level-message-passing-with-hierarchical	0.6866±0.0038
exphormer-sparse-transformers-for-graphs	0.6527±0.0043
where-did-the-gap-go-reassessing-the-long	0.6765±0.0047
masked-attention-is-all-you-need-for-graphs	0.6863±0.0044
on-the-connection-between-mpnn-and-graph	0.6685±0.0062
long-range-graph-benchmark	0.5498±0.0079
where-did-the-gap-go-reassessing-the-long	0.6860±0.0050
masked-attention-is-all-you-need-for-graphs	0.7071±0.0015
graph-transformers-without-positional	0.6414
long-range-graph-benchmark	0.5864±0.0077
long-range-graph-benchmark	0.6326±0.0126
a-generalization-of-vit-mlp-mixer-to-graphs	0.6942±0.0075
path-neural-networks-expressive-and-accurate	0.6816±0.0026
panda-expanded-width-aware-message-passing	0.6028±0.0031
molecular-fingerprints-are-strong-models-for	0.7460
where-did-the-gap-go-reassessing-the-long	0.6621±0.0067
masked-attention-is-all-you-need-for-graphs	0.7357±0.0036
learning-probabilistic-symmetrization-for-1	0.6575
recipe-for-a-general-powerful-scalable-graph	0.6535±0.0041
long-range-graph-benchmark	0.6069±0.0035
long-range-graph-benchmark	0.6384±0.0121
a-generalization-of-vit-mlp-mixer-to-graphs	0.6921±0.0054
molecular-fingerprints-are-strong-models-for	0.7318
learning-long-range-dependencies-on-graphs	0.7096 ± 0.0078
where-did-the-gap-go-reassessing-the-long	0.6534±0.0091
simple-and-deep-graph-convolutional-networks-1	0.5543±0.0078
graph-inductive-biases-in-transformers	0.6988±0.0082
transformers-for-capturing-multi-level-graph	0.7156±0.0058
topology-informed-graph-transformer	0.6679
multiresolution-graph-transformers-and	0.6817±0.0064
spatio-spectral-graph-neural-networks	0.7311±0.0066
long-range-graph-benchmark	0.6439±0.0075
molecular-fingerprints-are-strong-models-for	0.7311
masked-attention-is-all-you-need-for-graphs	0.7479
molecular-topological-profile-moltop-simple-1	0.6459 ± 0.0005
cin-enhancing-topological-message-passing	0.6569±0.0117