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Graph Classification On Collab

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

模型名称
Accuracy
Paper TitleRepository
GFN-light81.34%Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
GMT80.74%Accurate Learning of Graph Representations with Graph Multiset Pooling
G_DenseNet83.16%When Work Matters: Transforming Classical Network Structures to Graph CNN-
DGCNN68.34%DGCNN: Disordered Graph Convolutional Neural Network Based on the Gaussian Mixture Model-
DGCNN73.76%An End-to-End Deep Learning Architecture for Graph Classification
sGIN80.71%Mutual Information Maximization in Graph Neural Networks
PPGN81.38%Provably Powerful Graph Networks
GCN80.6%Fast Graph Representation Learning with PyTorch Geometric
GraphSAGE73.9%A Fair Comparison of Graph Neural Networks for Graph Classification
R-GIN + PANDA77.8%PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
R-GCN + PANDA71.4%PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
hGANet77.48%Graph Representation Learning via Hard and Channel-Wise Attention Networks
GCN + PANDA68.4%PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Graph U-Nets77.56%Graph U-Nets
1-NMFPool65.0%A Non-Negative Factorization approach to node pooling in Graph Convolutional Neural Networks-
CT-Layer69.87%DiffWire: Inductive Graph Rewiring via the Lovász Bound
U2GNN (Unsupervised)95.62%Universal Graph Transformer Self-Attention Networks
U2GNN77.84%Universal Graph Transformer Self-Attention Networks
FactorGCN81.2%Factorizable Graph Convolutional Networks
NDP79.1%Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
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