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SOTA
Graph Classification
Graph Classification On Ptc
Graph Classification On Ptc
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
Repository
U2GNN (Unsupervised)
91.81%
Universal Graph Transformer Self-Attention Networks
U2GNN
69.63%
Universal Graph Transformer Self-Attention Networks
δ-2-LWL
62.70%
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
DGA
71.24%
Discriminative Graph Autoencoder
-
WWL
66.31%
Wasserstein Weisfeiler-Lehman Graph Kernels
DGCNN
65.43%
DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model
-
Deep WL SGN(0,1,2)
65.88%
Subgraph Networks with Application to Structural Feature Space Expansion
-
TFGW ADJ (L=2)
72.4%
Template based Graph Neural Network with Optimal Transport Distances
DDGK
63.14%
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
DUGNN
74.7%
Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
UGraphEmb
72.54%
Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
Spec-GN
68.05%
A New Perspective on the Effects of Spectrum in Graph Neural Networks
SPI-GCN
56.41%
SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network
-
SF + RFC
62.8%
A Simple Baseline Algorithm for Graph Classification
TREE-G
59.1%
TREE-G: Decision Trees Contesting Graph Neural Networks
GIUNet
85.7%
Graph isomorphism UNet
cGANet
63.53%
Graph Representation Learning via Hard and Channel-Wise Attention Networks
graph2vec
60.17% ± 6.86%
graph2vec: Learning Distributed Representations of Graphs
CAN
72.8%
Cell Attention Networks
CIN++
73.2%
CIN++: Enhancing Topological Message Passing
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