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Graph Classification On Imdb M

評価指標

Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
Accuracy
Paper TitleRepository
DGCNN47.83%An End-to-End Deep Learning Architecture for Graph Classification
GraphSAGE47.6%A Fair Comparison of Graph Neural Networks for Graph Classification-
GIN-052.3%How Powerful are Graph Neural Networks?-
1-WL Kernel51.5%Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks-
G-Tuning-Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns-
U2GNN (Unsupervised)89.2%Universal Graph Transformer Self-Attention Networks-
GIUNet54%Graph isomorphism UNet
Graph-JEPA50.69%Graph-level Representation Learning with Joint-Embedding Predictive Architectures-
TREE-G56.4%TREE-G: Decision Trees Contesting Graph Neural Networks-
MEWISPool56.23%Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks-
GFN-light51.20%Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification-
SPI-GCN44.13%SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network-
DGCNN (sum)42.76%An End-to-End Deep Learning Architecture for Graph Classification
UGraphEmb50.06%Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity-
k-GNN49.5%Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks-
GMT50.66%Accurate Learning of Graph Representations with Graph Multiset Pooling-
G_ResNet54.53%When Work Matters: Transforming Classical Network Structures to Graph CNN-
DropGIN51.4%DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks-
SEG-BERT53.4%Segmented Graph-Bert for Graph Instance Modeling-
δ-2-LWL50.5%Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings-
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Graph Classification On Imdb M | SOTA | HyperAI超神経