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المنصة
الرئيسية
SOTA
تصنيف الرسم البياني
Graph Classification On Ptc
Graph Classification On Ptc
المقاييس
Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
Accuracy
Paper Title
U2GNN (Unsupervised)
91.81%
Universal Graph Transformer Self-Attention Networks
GIUNet
85.7%
Graph isomorphism UNet
GIC
77.64%
Gaussian-Induced Convolution for Graphs
DUGNN
74.7%
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
sGIN
73.56%
Mutual Information Maximization in Graph Neural Networks
UGraphEmb-F
73.56%
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
G_DenseNet
73.24%
When Work Matters: Transforming Classical Network Structures to Graph CNN
CIN++
73.2%
CIN++: Enhancing Topological Message Passing
CAN
72.8%
Cell Attention Networks
UGraphEmb
72.54%
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
TFGW ADJ (L=2)
72.4%
Template based Graph Neural Network with Optimal Transport Distances
DGA
71.24%
Discriminative Graph Autoencoder
U2GNN
69.63%
Universal Graph Transformer Self-Attention Networks
BC + Capsules
69%
Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations
SEG-BERT
68.86%
Segmented Graph-Bert for Graph Instance Modeling
Spec-GN
68.05%
A New Perspective on the Effects of Spectrum in Graph Neural Networks
WEGL
67.5%
Wasserstein Embedding for Graph Learning
WWL
66.31%
Wasserstein Weisfeiler-Lehman Graph Kernels
DropGIN
66.3%
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
PPGN
66.17%
Provably Powerful Graph Networks
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