OpenCodePapers

node-classification-on-reddit

Node Classification
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PaperCodeAccuracyMicro-F1ModelNameReleaseDate
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling✓ Link97.17%BNS-GCN2022-03-21
Communication-Free Distributed GNN Training with Vertex Cut97.14±0.02%CoFree-GNN2023-08-06
Decoupling the Depth and Scope of Graph Neural Networks✓ Link97.13%shaDow-GAT2022-01-19
Decoupling the Depth and Scope of Graph Neural Networks✓ Link97.03%shaDow-SAGE2022-01-19
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification✓ Link97.02%JKNet+DropEdge2019-07-25
GraphSAINT: Graph Sampling Based Inductive Learning Method✓ Link97.0%GraphSAINT2019-07-10
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking✓ Link96.65%EnGCN2022-10-14
SIGN: Scalable Inception Graph Neural Networks✓ Link96.60%SIGN2020-04-23
Adaptive Sampling Towards Fast Graph Representation Learning✓ Link96.27%ASGCN2018-09-14
Dimensionality Reduction Meets Message Passing for Graph Node Embeddings✓ Link96.26 ± 0.02%PCAPass + XGBoost2022-02-01
Simple Spectral Graph Convolution✓ Link95.3SSGC2021-01-01
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization✓ Link94.5 ± .0024VQ-GNN (SAGE-Mean)2021-10-27
Inductive Representation Learning on Large Graphs✓ Link94.32%GraphSAGE2017-06-07
FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling✓ Link93.70%FastGCN2018-01-30
Deeper-GXX: Deepening Arbitrary GNNs81.06±1.18%TGCL+ResNet2021-10-26
Deep Graph Contrastive Representation Learning✓ Link94.2 ± 0.0GRACE2020-06-07
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