Classic
Structure
1Graphs
232,965Nodes
11,606,919Edges
41Classes
602Node Feature Dim
Coverage
588Results
1Variants
90Papers
- Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering
- CO-EVOLVE: Bidirectional Co-Evolution of Graph Structure and Semantics for Heterophilous Learning CO-EVOLVE: Bidirectional Co-Evolution of Graph Structure and Semantics for Heterophilous Learning
- Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction
- NeurIPS 2025: Semi-supervised Graph Anomaly Detection via Robust Homophily Learning Semi-supervised Graph Anomaly Detection via Robust Homophily Learning NeurIPS 2025
- Dynamic Graph Condensation Dynamic Graph Condensation
- Are Large Language Models Good Temporal Graph Learners? Are Large Language Models Good Temporal Graph Learners?
- Efficient Mixed Precision Quantization in Graph Neural Networks Efficient Mixed Precision Quantization in Graph Neural Networks
- Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts
- ICLR 2025: Accurate and Scalable Graph Neural Networks via Message Invariance Accurate and Scalable Graph Neural Networks via Message Invariance ICLR 2025
- THeGCN: Temporal Heterophilic Graph Convolutional Network THeGCN: Temporal Heterophilic Graph Convolutional Network
- IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
- Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
- TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks
- NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
- AAAI 2024: A Scalable and Effective Alternative to Graph Transformers A Scalable and Effective Alternative to Graph Transformers AAAI 2024
- NeurIPS 2024: on Large Graphs using Intersecting Communities on Large Graphs using Intersecting Communities NeurIPS 2024
- ICLR 2024: Fully-inductive Node Classification on Arbitrary Graphs Fully-inductive Node Classification on Arbitrary Graphs ICLR 2024
- ICLR 2024: Node-Time Conditional Prompt Learning in Dynamic Graphs Node-Time Conditional Prompt Learning in Dynamic Graphs ICLR 2024
- WWW 2024: Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition WWW 2024
- Simple Graph Condensation Simple Graph Condensation
- AAAI 2024: Rethinking Graph Masked Autoencoders through Alignment and Uniformity Rethinking Graph Masked Autoencoders through Alignment and Uniformity AAAI 2024
- WWW 2024: EXGC: Bridging Efficiency and Explainability in Graph Condensation EXGC: Bridging Efficiency and Explainability in Graph Condensation WWW 2024
- WWW 2024: Disentangled Condensation for Large-scale Graphs Disentangled Condensation for Large-scale Graphs WWW 2024
- Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
- ICML 2023: Graph Distillation with Eigenbasis Matching Graph Distillation with Eigenbasis Matching ICML 2023
- FedGTA: Topology-aware Averaging for Federated Graph Learning FedGTA: Topology-aware Averaging for Federated Graph Learning
- Graph Condensation for Inductive Node Representation Learning Graph Condensation for Inductive Node Representation Learning
- Collaborative Graph Neural Networks for Attributed Network Embedding Collaborative Graph Neural Networks for Attributed Network Embedding
- CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
- NeurIPS 2023: GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection NeurIPS 2023
- ICML 2023: Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication ICML 2023
- NeurIPS 2023: Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data NeurIPS 2023
- Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
- Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs Less Can Be More: Unsupervised Graph Pruning for Large-scale Dynamic Graphs
- IJCAI 2023: Hierarchical Transformer for Scalable Graph Learning Hierarchical Transformer for Scalable Graph Learning IJCAI 2023
- WWW 2023: Node-wise Diffusion for Scalable Graph Learning Node-wise Diffusion for Scalable Graph Learning WWW 2023
- ICLR 2023: LMC: Fast Training of GNNs via subgraph-wise sampling with Provable Convergence LMC: Fast Training of GNNs via subgraph-wise sampling with Provable Convergence ICLR 2023
- 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
- From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm
- ICML 2022: RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations RSC: Accelerate Graph Neural Networks Training via Randomized Sparse Computations ICML 2022
- NeurIPS 2022: A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking NeurIPS 2022
- GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials
- NeurIPS 2022: Provably expressive temporal graph networks Provably expressive temporal graph networks NeurIPS 2022
- Relational Self-Supervised Learning on Graphs Relational Self-Supervised Learning on Graphs
- Generative Subgraph Contrast for Self-Supervised Graph Representation Learning Generative Subgraph Contrast for Self-Supervised Graph Representation Learning
- SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
- Deformable Graph Transformer Deformable Graph Transformer
- KDD 2022: Graph Attention Multi-Layer Perceptron Graph Attention Multi-Layer Perceptron KDD 2022
- KDD 2022: Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation KDD 2022
- KDD 2022: GraphMAE: Self-Supervised Masked Graph Autoencoders GraphMAE: Self-Supervised Masked Graph Autoencoders KDD 2022
- TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
- GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation
- 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
- Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
- ICML 2022: Self-Supervised Representation Learning via Latent Graph Prediction Self-Supervised Representation Learning via Latent Graph Prediction ICML 2022
- Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
- Graph Representation Learning via Aggregation Enhancement Graph Representation Learning via Aggregation Enhancement
- NeurIPS 2022: Contrastive Laplacian Eigenmaps Contrastive Laplacian Eigenmaps NeurIPS 2022
- Adaptive Label Smoothing To Regularize Large-Scale Graph Training Adaptive Label Smoothing To Regularize Large-Scale Graph Training
- KDD 2021: ROD: Reception-aware Online Distillation for Sparse Graphs ROD: Reception-aware Online Distillation for Sparse Graphs KDD 2021
- Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
- Accelerating Large Scale Real-Time GNN Inference using Channel Pruning Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
- GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
- Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced Training
- DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
- Bag of Tricks for Node Classification with Graph Neural Networks Bag of Tricks for Node Classification with Graph Neural Networks
- Sampling methods for efficient training of graph convolutional networks: A survey Sampling methods for efficient training of graph convolutional networks: A survey
- NeurIPS 2021: Dissecting the Diffusion Process in Linear Graph Convolutional Networks Dissecting the Diffusion Process in Linear Graph Convolutional Networks NeurIPS 2021
- TMLR 2021: Calibrating and Improving Graph Contrastive Learning Calibrating and Improving Graph Contrastive Learning TMLR 2021
- Bi-GCN: Binary Graph Convolutional Network Bi-GCN: Binary Graph Convolutional Network
- Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
- Adversarial Immunization for Certifiable Robustness on Graphs Adversarial Immunization for Certifiable Robustness on Graphs
- Self-supervised Learning on Graphs: Deep Insights and New Directions Self-supervised Learning on Graphs: Deep Insights and New Directions
- Deep Graph Contrastive Representation Learning Deep Graph Contrastive Representation Learning
- SIGN: Scalable Inception Graph Neural Networks SIGN: Scalable Inception Graph Neural Networks
- L$^2$-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks L$^2$-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
- WWW 2020: Graph Representation Learning via Graphical Mutual Information Maximization Graph Representation Learning via Graphical Mutual Information Maximization WWW 2020
- NeurIPS 2019: Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks NeurIPS 2019
- AAAI 2019: Graph Few-shot Learning via Knowledge Transfer Graph Few-shot Learning via Knowledge Transfer AAAI 2019
- ICLR 2019: GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding ICLR 2019
- ICLR 2019: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification DropEdge: Towards Deep Graph Convolutional Networks on Node Classification ICLR 2019
- ICLR 2019: GraphSAINT: Graph Sampling Based Inductive Learning Method GraphSAINT: Graph Sampling Based Inductive Learning Method ICLR 2019
- Revisiting Graph Neural Networks: All We Have is Low-Pass Filters Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
- KDD 2019: Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks KDD 2019
- A Comprehensive Survey on Graph Neural Networks A Comprehensive Survey on Graph Neural Networks
- Deep Learning on Graphs: A Survey Deep Learning on Graphs: A Survey
- NeurIPS 2018: Adaptive Sampling Towards Fast Graph Representation Learning Adaptive Sampling Towards Fast Graph Representation Learning NeurIPS 2018
- GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs
- ICLR 2018: FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling ICLR 2018
- NeurIPS 2017: Inductive Representation Learning on Large Graphs Inductive Representation Learning on Large Graphs NeurIPS 2017
41-class node classification on a post-interaction graph, predicting the subreddit/community of each post from text-derived features. Evaluated by F1.
Dataset details
Task
Reddit posts node classification (41 community labels) from the GraphSAGE paper. The primary catalog metric is F1.
Data
Reddit posts node classification (41 community labels) from the GraphSAGE 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 F1 (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 | F1 ↑ | AUC | AUC-ROC | AUPRC | AUROC | Accuracy | Accuracy (%) | Accuracy (percentage; caption L936 states only 'prediction performance', no explicit F1/macro-F1 definition) | Accuracy (percentage; no explicit metric definition in source) | Accuracy degradation | F1-Micro | F1-macro | F1-micro | MRR | Macro-F1 | Micro-F1 | accuracy (percent, no explicit header) | accuracy (percent, no explicit header; source follows benchmark metrics per L381) | micro-averaged F1 (percent) | Paper |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | EnGCN | 0.9714
±
0.0003 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 2 | SAGN+SLE | 0.9710
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 3 | GAMLP(JK) | 0.9704
±
0.0001 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 4 | GraphSAINT | 0.9700 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 5 | HopGNN | 0.9698
±
0.0004 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 6 | SAGN | 0.9690
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 7 | GAAN (2018) | 0.9683 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 8 | SIGN | 0.9680
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 9 | GAMLP | 0.9673
±
0.0003 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 10 | Neighbor2Seq+Conv | 0.9670
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 11 | Neighbor2Seq+Attn | 0.9670
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 12 | rf | 0.9662
±
0.0100 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 13 | GraphSAGE Ensemble | 0.9661 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 14 | GraphSAINT-EDGE | 0.9660
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 15 | GraphSAINT-RW | 0.9660
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 16 | Cluster-GCN | 0.9660 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 17 | GraphSAINT | 0.9660
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 18 | SIGN(2,0,0) | 0.9660 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 19 | GraphSAGE | 0.9660 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 20 | LASSO 2x | 0.9660 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 21 | Cluster-GCN (2019) | 0.9660 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 22 | JK-Nets | 0.9650 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 23 | GraphSAGE | 0.9650
±
0.0003 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 24 | SAGN | 0.9648
±
0.0003 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 25 | S-GCN | 0.9640
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 26 | GraphSAINT-MRW | 0.9640
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 27 | Stochastic-GCN | 0.9640
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 28 | AS-GCN | 0.9640
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 29 | LASSO 4x | 0.9640 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 30 | LASSO | 0.9640 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 31 | GaAN | 0.9636
±
0.0003 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 32 | StochasticGCN | 0.9630
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 33 | Adapt | 0.9630
±
0.0030 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 34 | VR-GCN | 0.9630 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 35 | StoGCN (2018) | 0.9630 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 36 | Huang et al. (2018) | 0.9627 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 37 | GraphSAINT-Node | 0.9620
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 38 | Attention-only | 0.9619
±
0.0007 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 39 | HAT-GAE | 0.9606
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 40 | AdaGCN | 0.9605
±
0.0000 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 41 | GraphMAE | 0.9601
±
0.0008 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 42 | GAT | 0.9600
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 43 | VRGCN | 0.9600 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 44 | Parallelized Graph Sampling | 0.9600
±
0.0020 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 45 | SIGN | 0.9595
±
0.0002 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 46 | GraphSAINT | 0.9590
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 47 | LASSO 8x | 0.9590 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 48 | Pairwise+sigmoid | 0.9586
±
0.0008 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 49 | AS-GCN | 0.9580
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 50 | GaAN-mean | 0.9580
±
0.0010 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time