Classic
Coauthor-CS
Structure
1Graphs
18,333Nodes
81,894Edges
15Classes
6805Node Feature Dim
0.832Homophily
Coverage
281Results
1Variants
44Papers
- Graph Navier–Stokes Networks Graph Navier–Stokes Networks
- Adaptive Negative Scheduling for Graph Contrastive Learning Adaptive Negative Scheduling for Graph Contrastive Learning
- Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow
- CORE: Contrastive Masked Feature Reconstruction on Graphs CORE: Contrastive Masked Feature Reconstruction on Graphs
- Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
- IJCAI 2025: Rethinking Graph Contrastive Learning through Relative Similarity Preservation Rethinking Graph Contrastive Learning through Relative Similarity Preservation IJCAI 2025
- WWW 2025: Graph Wave Networks Graph Wave Networks WWW 2025
- TMLR 2025: Diffusion on Graph: Augmentation of Graph Structure for Node Classification Diffusion on Graph: Augmentation of Graph Structure for Node Classification TMLR 2025
- TMLR 2025: A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning TMLR 2025
- PyG-SSL: A Graph Self-Supervised Learning Toolkit PyG-SSL: A Graph Self-Supervised Learning Toolkit
- Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Revisiting the Necessity of Graph Learning and Common Graph Benchmarks
- Negative-Free Self-Supervised Gaussian Embedding of Graphs Negative-Free Self-Supervised Gaussian Embedding of Graphs
- Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
- ICML 2024: How Interpretable Are Interpretable Graph Neural Networks? How Interpretable Are Interpretable Graph Neural Networks? ICML 2024
- Graph Neural Aggregation-diffusion with Metastability Graph Neural Aggregation-diffusion with Metastability
- Smoothed Graph Contrastive Learning via Seamless Proximity Integration Smoothed Graph Contrastive Learning via Seamless Proximity Integration
- Rethinking and Simplifying Bootstrapped Graph Latents Rethinking and Simplifying Bootstrapped Graph Latents
- NeurIPS 2023: GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation NeurIPS 2023
- On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
- Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks
- HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption
- AAAI 2022: MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning AAAI 2022
- ICML 2022: TIDE: Time Derivative Diffusion for Deep Learning on Graphs TIDE: Time Derivative Diffusion for Deep Learning on Graphs ICML 2022
- AAAI 2022: Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating AAAI 2022
- ICLR 2022: Spectral Augmentation for Self-Supervised Learning on Graphs Spectral Augmentation for Self-Supervised Learning on Graphs ICLR 2022
- ICLR 2022: ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks ICLR 2022
- KDD 2022: Graph Attention Multi-Layer Perceptron Graph Attention Multi-Layer Perceptron KDD 2022
- Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective
- ICML 2022: Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning Omni-Granular Ego-Semantic Propagation for Self-Supervised Graph Representation Learning ICML 2022
- Embedding Graphs on Grassmann Manifold Embedding Graphs on Grassmann Manifold
- WWW 2022: PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm WWW 2022
- AAAI 2022: Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning AAAI 2022
- Learning Graph Augmentations to Learn Graph Representations Learning Graph Augmentations to Learn Graph Representations
- Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
- WWW 2021: Graph Communal Contrastive Learning Graph Communal Contrastive Learning WWW 2021
- NeurIPS 2021: Beltrami Flow and Neural Diffusion on Graphs Beltrami Flow and Neural Diffusion on Graphs NeurIPS 2021
- KDD 2021: ROD: Reception-aware Online Distillation for Sparse Graphs ROD: Reception-aware Online Distillation for Sparse Graphs KDD 2021
- WWW 2020: Graph Contrastive Learning with Adaptive Augmentation Graph Contrastive Learning with Adaptive Augmentation WWW 2020
- A Unified View on Graph Neural Networks as Graph Signal Denoising A Unified View on Graph Neural Networks as Graph Signal Denoising
- Learning on Attribute-Missing Graphs Learning on Attribute-Missing Graphs
- NeurIPS 2020: Towards Deeper Graph Neural Networks with Differentiable Group Normalization Towards Deeper Graph Neural Networks with Differentiable Group Normalization NeurIPS 2020
- Spectral Graph Attention Network with Fast Eigen-approximation Spectral Graph Attention Network with Fast Eigen-approximation
- Unifying Graph Convolutional Neural Networks and Label Propagation Unifying Graph Convolutional Neural Networks and Label Propagation
- GraphMix: Improved Training of GNNs for Semi-Supervised Learning GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Node classification on one coauthorship graph, predicting each author’s computer-science field from paper-keyword features. Evaluated by Accuracy.
Dataset details
Task
Co-authorship graph (CS) node classification from the “Pitfalls of Graph Neural Network Evaluation” paper. The primary catalog metric is Accuracy.
Data
Co-authorship graph (CS) node classification from the “Pitfalls of Graph Neural Network Evaluation” paper.
Features
The representation follows the cited PyG loader or benchmark release. Node and edge fields are dataset-specific; preprocessing, graph direction, and any feature normalization must be kept fixed when reproducing a result.
Splits and evaluation
The catalog records these protocols or variants: Standard split. Evaluation uses Accuracy (higher is better). Exact masks or folds must come from the cited release.
Quirks and pitfalls
The same short dataset name is used for incompatible processed artifacts and split protocols. Report the loader/version, directedness, feature preprocessing, and exact masks; transductive results should not be described as inductive.
Sources
SOTA Progression
new SOTA
matches SOTA at that time
other proposed result
other baseline result
Leaderboard
| # | Model | Accuracy ↑ | F1 | Macro-F1 | Micro-F1 | Prediction Acc. | Paper |
|---|---|---|---|---|---|---|---|
| 1 | DoG | 0.9690
±
0.0030 | — | — | — | — | |
| 2 | GWN-fa | 0.9667
±
0.0026 | — | — | — | — | |
| 3 | GWN-sym | 0.9666
±
0.0026 | — | — | — | — | |
| 4 | CGNN | 0.9621
±
0.0032 | — | — | — | — | |
| 5 | GADC | 0.9616
±
0.0029 | — | — | — | — | |
| 6 | GraphSAGE | 0.9593
±
0.0025 | — | — | — | — | |
| 7 | ADC | 0.9582
±
0.0015 | — | — | — | — | |
| 8 | Exphormer | 0.9580
±
0.0020 | — | — | — | — | |
| 9 | FAGCN | 0.9579 | — | — | — | — | |
| 10 | GRAND | 0.9577
±
0.0028 | — | — | — | — | |
| 11 | OEPG | 0.9540
±
0.0010 | — | — | — | — | |
| 12 | FOSSIL v2 | 0.9504
±
0.0003 | — | — | — | — | |
| 13 | G-Zoom | 0.9490
±
0.0030 | — | — | — | — | |
| 14 | LaplaceGNN | 0.9486
±
0.0016 | — | — | — | — | |
| 15 | GDC | 0.9482
±
0.0022 | — | — | — | — | |
| 16 | USGCL | 0.9480
±
0.0050 | — | — | — | — | |
| 17 | GCN-LPA | 0.9480
±
0.0040 | — | — | — | — | |
| 18 | GREET | 0.9465
±
0.0018 | — | — | — | — | |
| 19 | FOSSIL | 0.9449
±
0.0004 | — | — | — | — | |
| 20 | AdNGCL | 0.9430
±
0.0023 | — | — | — | — | |
| 21 | RC-UFG (Hom) | 0.9420
±
0.0090 | — | — | — | — | |
| 22 | MA-GCL | 0.9419
±
0.0010 | — | — | — | — | |
| 23 | SubG-Con | 0.9400
±
0.0060 | — | — | — | — | |
| 24 | True attributes | 0.9396 | — | — | — | — | |
| 25 | ADA-UGNN | 0.9392
±
0.0060 | — | — | — | — | |
| 26 | MVGRL-SPAN | 0.9391
±
0.0024 | — | — | — | — | |
| 27 | APPNP | 0.9384
±
0.0050 | — | — | — | — | |
| 28 | GraphTP-F | 0.9381
±
0.0002 | — | — | — | — | |
| 29 | GAT | 0.9380
±
0.0040 | — | — | — | — | |
| 30 | S^2GC | 0.9380
±
0.0010 | — | — | — | — | |
| 31 | BGRL-SPAN | 0.9377
±
0.0021 | — | — | — | — | |
| 32 | ReGCL | 0.9370
±
0.0030 | — | — | — | — | |
| 33 | SGFormer | 0.9370
±
0.0040 | — | — | — | — | |
| 34 | ProGCL | 0.9367
±
0.0012 | — | — | — | — | |
| 35 | ProGCL-mix | 0.9367
±
0.0012 | — | — | — | — | |
| 36 | LG2AR | 0.9360
±
0.0030 | — | — | — | — | |
| 37 | LG2AR + GRU | 0.9360
±
0.0030 | — | — | — | — | |
| 38 | BNLL | 0.9360
±
0.0003 | — | — | — | — | |
| 39 | ABGML | 0.9360
±
0.0020 | — | — | — | — | |
| 40 | GCN | 0.9360
±
0.0150 | — | — | — | — | |
| 41 | RelGCL_Pair | 0.9353
±
0.0012 | — | — | — | — | |
| 42 | ProGCL-weight | 0.9351
±
0.0006 | — | — | — | — | |
| 43 | RelGCL_List | 0.9350
±
0.0012 | — | — | — | — | |
| 44 | SSGE | 0.9346
±
0.0045 | — | — | — | — | |
| 45 | SimGRACE | 0.9345
±
0.0040 | — | — | — | — | |
| 46 | ROD | 0.9340
±
0.0050 | — | — | — | — | |
| 47 | CCA-SSG | 0.9337
±
0.0020 | — | — | — | — | |
| 48 | ARIEL | 0.9335
±
0.0000 | — | — | — | — | |
| 49 | AutoSSL | 0.9335
±
0.0009 | — | — | — | — | |
| 50 | GCA | 0.9332
±
0.0012 | — | — | — | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time