الرئيسية الأخبار أحدث الأوراق البحثية الدروس مجموعات البيانات الموسوعة SOTA نماذج LLM لوحة الأداء GPU الفعاليات

العربية

Node Classification On Reddit

المقاييس

Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	Accuracy	Paper Title	Repository
shaDow-SAGE	97.03%	Decoupling the Depth and Scope of Graph Neural Networks	-
BNS-GCN	97.17%	BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling
FastGCN	93.70%	FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
EnGCN	96.65%	A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
CoFree-GNN	97.14±0.02%	Communication-Free Distributed GNN Training with Vertex Cut	-
ASGCN	96.27%	Adaptive Sampling Towards Fast Graph Representation Learning
TGCL+ResNet	81.06±1.18%	Deeper-GXX: Deepening Arbitrary GNNs	-
GraphSAGE	94.32%	Inductive Representation Learning on Large Graphs
SSGC	95.3	Simple Spectral Graph Convolution
SIGN	96.60%	SIGN: Scalable Inception Graph Neural Networks
GRACE	-	Deep Graph Contrastive Representation Learning
GraphSAINT	97.0%	GraphSAINT: Graph Sampling Based Inductive Learning Method
JKNet+DropEdge	97.02%	DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
VQ-GNN (SAGE-Mean)	94.5 ± .0024	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
PCAPass + XGBoost	96.26 ± 0.02%	Dimensionality Reduction Meets Message Passing for Graph Node Embeddings	-
shaDow-GAT	97.13%	Decoupling the Depth and Scope of Graph Neural Networks	-

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