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Graph Classification On Reddit B

Metrics

Accuracy

Results

Performance results of various models on this benchmark

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

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Graph Classification On Reddit B | SOTA | HyperAI