Graph Classification On Imdb M

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
U2GNN (Unsupervised)	89.2%	Universal Graph Transformer Self-Attention Networks
TFGW ADJ (L=2)	56.8%	Template based Graph Neural Network with Optimal Transport Distances
TREE-G	56.4%	TREE-G: Decision Trees Contesting Graph Neural Networks
MEWISPool	56.23%	Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks
DUGNN	56.10%	Learning Universal Graph Neural Network Embeddings With Aid Of Transfer Learning
G_ResNet	54.53%	When Work Matters: Transforming Classical Network Structures to Graph CNN
sGIN	54.52%	Mutual Information Maximization in Graph Neural Networks
GIUNet	54%	Graph isomorphism UNet
U2GNN	53.60%	Universal Graph Transformer Self-Attention Networks
SEG-BERT	53.4%	Segmented Graph-Bert for Graph Instance Modeling
GIN-0	52.3%	How Powerful are Graph Neural Networks?
WEGL	52%	Wasserstein Embedding for Graph Learning
GFN	51.80%	Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
1-WL Kernel	51.5%	Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
DropGIN	51.4%	DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks
GFN-light	51.20%	Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
UGraphEmb-F	50.97%	Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity
Graph-JEPA	50.69%	Graph-level Representation Learning with Joint-Embedding Predictive Architectures
GMT	50.66%	Accurate Learning of Graph Representations with Graph Multiset Pooling
GDL	50.64%	Online Graph Dictionary Learning

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