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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Graph Classification On Reddit B

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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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Graph Classification On Reddit B | SOTA | HyperAI초신경