HyperAI超神经

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HyperAI超神经

Node Classification On Reddit

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	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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