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Node Classification On Reddit

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
shaDow-SAGE97.03%Decoupling the Depth and Scope of Graph Neural Networks-
BNS-GCN97.17%BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling-
FastGCN93.70%FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling-
EnGCN96.65%A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking-
CoFree-GNN97.14±0.02%Communication-Free Distributed GNN Training with Vertex Cut-
ASGCN96.27%Adaptive Sampling Towards Fast Graph Representation Learning-
TGCL+ResNet81.06±1.18%Deeper-GXX: Deepening Arbitrary GNNs-
GraphSAGE94.32%Inductive Representation Learning on Large Graphs-
SSGC95.3Simple Spectral Graph Convolution
SIGN96.60%SIGN: Scalable Inception Graph Neural Networks-
GRACE-Deep Graph Contrastive Representation Learning-
GraphSAINT97.0%GraphSAINT: Graph Sampling Based Inductive Learning Method-
JKNet+DropEdge97.02%DropEdge: Towards Deep Graph Convolutional Networks on Node Classification-
VQ-GNN (SAGE-Mean)94.5 ± .0024VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization-
PCAPass + XGBoost96.26 ± 0.02%Dimensionality Reduction Meets Message Passing for Graph Node Embeddings-
shaDow-GAT97.13%Decoupling the Depth and Scope of Graph Neural Networks-
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Node Classification On Reddit | SOTA | HyperAI