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Graph Classification On Reddit B

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
CRaWl	93.15	Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
WEGL	92	Wasserstein Embedding for Graph Learning
δ-2-LWL	89.0	Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings
GAT-GC (f-Scaled)	92.57	Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
NDP	84.3	Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
Graph-JEPA	56.73	Graph-level Representation Learning with Joint-Embedding Predictive Architectures
GIN-0	92.4	How Powerful are Graph Neural Networks?
ApproxRepSet	80.3	Rep the Set: Neural Networks for Learning Set Representations
2-WL-GNN	89.4	A Novel Higher-order Weisfeiler-Lehman Graph Convolution
GraphSAGE	84.3	A Fair Comparison of Graph Neural Networks for Graph Classification
DiffPool	92.1	Fast Graph Representation Learning with PyTorch Geometric

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