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SOTA
Graph Classification
Graph Classification On Enzymes
Graph Classification On Enzymes
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Columns
Modellname
Accuracy
Paper Title
Repository
DAGCN
58.17%
DAGCN: Dual Attention Graph Convolutional Networks
UGT
67.22±3.92
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
Multigraph ChebNet
61.7%
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
ESA (Edge set attention, no positional encodings)
79.423±1.658
An end-to-end attention-based approach for learning on graphs
-
Evolution of Graph Classifiers
55.67
Evolution of Graph Classifiers
CapsGNN
54.67%
Capsule Graph Neural Network
TFGW SP (L=2)
75.1
Template based Graph Neural Network with Optimal Transport Distances
GATv2
77.987±2.112
How Attentive are Graph Attention Networks?
GCN
73.466±4.372
Semi-Supervised Classification with Graph Convolutional Networks
GIN + PANDA
46.2
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
WEGL
60.5
Wasserstein Embedding for Graph Learning
DEMO-Net(weight)
27.2
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
S2V (with 2 DiffPool)
63.33%
Hierarchical Graph Representation Learning with Differentiable Pooling
GFN-light
69.50%
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
GDL-g (SP)
71.47
Online Graph Dictionary Learning
Fea2Fea-s2
48.5
Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
ECC (5 scores)
52.67%
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
DGK
53.43%
Deep Graph Kernels
-
GraphSAGE
58.2%
A Fair Comparison of Graph Neural Networks for Graph Classification
Norm-GN
73.33
A New Perspective on the Effects of Spectrum in Graph Neural Networks
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