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Graph Classification On Dd

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
U2GNN (Unsupervised)95.67%Universal Graph Transformer Self-Attention Networks
MEWISPool84.33%Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
ESA (Edge set attention, no positional encodings)83.529±1.743An end-to-end attention-based approach for learning on graphs
DDGK83.14%DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Graph U-Nets82.43%Graph U-Nets
DUGNN82.40%Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
S2V (with 2 DiffPool)82.07%Hierarchical Graph Representation Learning with Differentiable Pooling
WKPI-kmeans82.0%Learning metrics for persistence-based summaries and applications for graph classification
hGANet81.71%Graph Representation Learning via Hard and Channel-Wise Attention Networks
HGP-SL80.96%Hierarchical Graph Pooling with Structure Learning
SEAL-SAGE80.88%Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
GNN (DiffPool)80.64%Hierarchical Graph Representation Learning with Differentiable Pooling
NERO80.45%Relation order histograms as a network embedding tool
U2GNN80.23%Universal Graph Transformer Self-Attention Networks
WWL79.69%Wasserstein Weisfeiler-Lehman Graph Kernels
GraphStar79.60%Graph Star Net for Generalized Multi-Task Learning
DGCNN79.37%An End-to-End Deep Learning Architecture for Graph Classification
PNA78.992±4.407Principal Neighbourhood Aggregation for Graph Nets
Propagation kernels (pk)78.8%Propagation kernels: efficient graph kernels from propagated information
GFN78.78%Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
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Graph Classification On Dd | SOTA | HyperAI