Classic
Amazon-Computers
Structure
1Graphs
13,752Nodes
245,861Edges
10Classes
767Node Feature Dim
0.7853Homophily
Coverage
702Results
1Variants
115Papers
- Graph Navier–Stokes Networks Graph Navier–Stokes Networks
- Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
- Adaptive Negative Scheduling for Graph Contrastive Learning Adaptive Negative Scheduling for Graph Contrastive Learning
- WWW 2026: VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention WWW 2026
- CORE: Contrastive Masked Feature Reconstruction on Graphs CORE: Contrastive Masked Feature Reconstruction on Graphs
- LoG 2025: Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization? Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization? LoG 2025
- Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering Rethinking Semi-Supervised Node Classification with Self-Supervised Graph Clustering
- NeurIPS 2025: Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models NeurIPS 2025
- Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
- Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
- KDD 2025: Discrepancy-Aware Graph Mask Auto-Encoder Discrepancy-Aware Graph Mask Auto-Encoder KDD 2025
- Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
- Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
- IJCAI 2025: Negative Metric Learning for Graphs Negative Metric Learning for Graphs IJCAI 2025
- IJCAI 2025: Rethinking Graph Contrastive Learning through Relative Similarity Preservation Rethinking Graph Contrastive Learning through Relative Similarity Preservation IJCAI 2025
- TMLR 2025: Heterophily-informed Message Passing Heterophily-informed Message Passing TMLR 2025
- WWW 2025: Graph Wave Networks Graph Wave Networks WWW 2025
- WWW 2025: Str-GCL: Structural Commonsense Driven Graph Contrastive Learning Str-GCL: Structural Commonsense Driven Graph Contrastive Learning WWW 2025
- WWW 2025: Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition WWW 2025
- GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
- ICML 2024: Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees ICML 2024
- Revisiting the Necessity of Graph Learning and Common Graph Benchmarks Revisiting the Necessity of Graph Learning and Common Graph Benchmarks
- NeurIPS 2024: Graph Neural Networks Need Cluster-Normalize-Activate Modules Graph Neural Networks Need Cluster-Normalize-Activate Modules NeurIPS 2024
- NeurIPS 2024: Even Sparser Graph Transformers Even Sparser Graph Transformers NeurIPS 2024
- NeurIPS 2024: Distributed-Order Fractional Graph Operating Network Distributed-Order Fractional Graph Operating Network NeurIPS 2024
- TMLR 2024: Sparse Decomposition of Graph Neural Networks Sparse Decomposition of Graph Neural Networks TMLR 2024
- LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space LAC: Graph Contrastive Learning with Learnable Augmentation in Continuous Space
- KDD 2024: Revisiting Graph Autoencoders as Implicit Contrastive Learners Revisiting Graph Autoencoders as Implicit Contrastive Learners KDD 2024
- ICML 2024: Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms Diss-l-ECT: Dissecting Graph Data with local Euler Characteristic Transforms ICML 2024
- Multiplex Graph Contrastive Learning with Soft Negatives Multiplex Graph Contrastive Learning with Soft Negatives
- Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning
- NeurIPS 2024: Non-convolutional Graph Neural Networks Non-convolutional Graph Neural Networks NeurIPS 2024
- Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift Topology Reorganized Graph Contrastive Learning with Mitigating Semantic Drift
- Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
- TMLR 2024: GraphFM: A generalist graph transformer that learns transferable representations across diverse domains GraphFM: A generalist graph transformer that learns transferable representations across diverse domains TMLR 2024
- NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
- UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
- NeurIPS 2024: Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights NeurIPS 2024
- Automatic Graph Topology-Aware Transformer Automatic Graph Topology-Aware Transformer
- NeurIPS 2024: HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning NeurIPS 2024
- ICML 2024: A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition ICML 2024
- ICLR 2024: Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND ICLR 2024
- Graph Neural Aggregation-diffusion with Metastability Graph Neural Aggregation-diffusion with Metastability
- NeurIPS 2024: Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision NeurIPS 2024
- ICLR 2024: Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks ICLR 2024
- ICLR 2024: Polynormer: Polynomial-Expressive Graph Transformer in Linear Time Polynormer: Polynomial-Expressive Graph Transformer in Linear Time ICLR 2024
- Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework. Teaching MLP More Graph Information: A Three-stage Multitask Knowledge Distillation Framework.
- Smoothed Graph Contrastive Learning via Seamless Proximity Integration Smoothed Graph Contrastive Learning via Seamless Proximity Integration
- TMLR 2024: Node Duplication Improves Cold-start Link Prediction Node Duplication Improves Cold-start Link Prediction TMLR 2024
- Training MLPs on Graphs without Supervision Training MLPs on Graphs without Supervision
- ICML 2024: Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective ICML 2024
- WWW 2024: Graph Contrastive Learning with Cohesive Subgraph Awareness Graph Contrastive Learning with Cohesive Subgraph Awareness WWW 2024
- ICLR 2024: FedGT: Federated Node Classification with Scalable Graph Transformer FedGT: Federated Node Classification with Scalable Graph Transformer ICLR 2024
- AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
- Rethinking and Simplifying Bootstrapped Graph Latents Rethinking and Simplifying Bootstrapped Graph Latents
- NeurIPS 2023: Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning NeurIPS 2023
- NeurIPS 2023: Simple and Asymmetric Graph Contrastive Learning without Augmentations Simple and Asymmetric Graph Contrastive Learning without Augmentations NeurIPS 2023
- BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
- WWW 2023: A Quasi-Wasserstein Loss for Learning Graph Neural Networks A Quasi-Wasserstein Loss for Learning Graph Neural Networks WWW 2023
- SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
- ICML 2023: Perfect Alignment May be Poisonous to Graph Contrastive Learning Perfect Alignment May be Poisonous to Graph Contrastive Learning ICML 2023
- FedGTA: Topology-aware Averaging for Federated Graph Learning FedGTA: Topology-aware Averaging for Federated Graph Learning
- Frameless Graph Knowledge Distillation Frameless Graph Knowledge Distillation
- ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
- KDD 2023: HomoGCL: Rethinking Homophily in Graph Contrastive Learning HomoGCL: Rethinking Homophily in Graph Contrastive Learning KDD 2023
- Clarify Confused Nodes via Separated Learning Clarify Confused Nodes via Separated Learning
- ICML 2023: Towards Deep Attention in Graph Neural Networks: Problems and Remedies Towards Deep Attention in Graph Neural Networks: Problems and Remedies ICML 2023
- Oversmoothing: A Nightmare for Graph Contrastive Learning? Oversmoothing: A Nightmare for Graph Contrastive Learning?
- ICML 2023: GC-Flow: A Graph-Based Flow Network for Effective Clustering GC-Flow: A Graph-Based Flow Network for Effective Clustering ICML 2023
- AAAI 2023: Union Subgraph Neural Networks Union Subgraph Neural Networks AAAI 2023
- ICML 2023: Feature Expansion for Graph Neural Networks Feature Expansion for Graph Neural Networks ICML 2023
- LON-GNN: Spectral GNNs with Learnable Orthonormal Basis LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
- WWW 2023: SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking WWW 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
- Localized Graph Contrastive Learning Localized Graph Contrastive Learning
- Node-oriented Spectral Filtering for Graph Neural Networks Node-oriented Spectral Filtering for Graph Neural Networks
- ICML 2022: TIDE: Time Derivative Diffusion for Deep Learning on Graphs TIDE: Time Derivative Diffusion for Deep Learning on Graphs ICML 2022
- NeurIPS 2022: On the Ability of Graph Neural Networks to Model Interactions Between Vertices On the Ability of Graph Neural Networks to Model Interactions Between Vertices NeurIPS 2022
- Unifying Graph Contrastive Learning with Flexible Contextual Scopes Unifying Graph Contrastive Learning with Flexible Contextual Scopes
- ICLR 2022: Spectral Augmentation for Self-Supervised Learning on Graphs Spectral Augmentation for Self-Supervised Learning on Graphs ICLR 2022
- Graph Soft-Contrastive Learning via Neighborhood Ranking Graph Soft-Contrastive Learning via Neighborhood Ranking
- Relational Self-Supervised Learning on Graphs Relational Self-Supervised Learning on Graphs
- AAAI 2022: Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning AAAI 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
- ICML 2022: Alternately Optimized Graph Neural Networks Alternately Optimized Graph Neural Networks ICML 2022
- Mixed Graph Contrastive Network for Semi-Supervised Node Classification Mixed Graph Contrastive Network for Semi-Supervised Node Classification
- NeurIPS 2022: Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination NeurIPS 2022
- 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
- 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
- Graph Masked Autoencoders with Transformers Graph Masked Autoencoders with Transformers
- Graph Representation Learning via Aggregation Enhancement Graph Representation Learning via Aggregation Enhancement
- A Piece-wise Polynomial Filtering Approach for Graph Neural Networks A Piece-wise Polynomial Filtering Approach for Graph Neural Networks
- AAAI 2021: Augmentation-Free Self-Supervised Learning on Graphs Augmentation-Free Self-Supervised Learning on Graphs AAAI 2021
- On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs with Missing Node Features On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs with Missing Node Features
- Barlow Graph Auto-Encoder for Unsupervised Network Embedding Barlow Graph Auto-Encoder for Unsupervised Network Embedding
- WWW 2021: Graph Communal Contrastive Learning Graph Communal Contrastive Learning WWW 2021
- NeurIPS 2021: Multi-view Contrastive Graph Clustering Multi-view Contrastive Graph Clustering NeurIPS 2021
- NeurIPS 2021: Beltrami Flow and Neural Diffusion on Graphs Beltrami Flow and Neural Diffusion on Graphs NeurIPS 2021
- ICLR 2021: Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation ICLR 2021
- Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
- KDD 2021: ROD: Reception-aware Online Distillation for Sparse Graphs ROD: Reception-aware Online Distillation for Sparse Graphs KDD 2021
- ICML 2021: Elastic Graph Neural Networks Elastic Graph Neural Networks ICML 2021
- WWW 2021: Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework WWW 2021
- TMLR 2021: Calibrating and Improving Graph Contrastive Learning Calibrating and Improving Graph Contrastive Learning TMLR 2021
- NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
- 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
- Learned Low Precision Graph Neural Networks Learned Low Precision Graph Neural Networks
- Graph Convolutional Networks for Graphs Containing Missing Features Graph Convolutional Networks for Graphs Containing Missing Features
- Directed Graph Convolutional Network Directed Graph Convolutional Network
- Adaptive Propagation Graph Convolutional Network Adaptive Propagation Graph Convolutional Network
- Graph-Revised Convolutional Network Graph-Revised Convolutional Network
- AAAI 2019: Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View AAAI 2019
Node classification on one co-purchase graph, predicting each product’s category from review-derived features and product links. Evaluated by Accuracy.
Dataset details
Task
Amazon co-purchase graph (Computers) node classification. The primary catalog metric is Accuracy.
Data
Amazon co-purchase graph (Computers) node classification.
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 ↑ | ACC | Accuracy (micro F1 per L1108) | F1 | Macro-F1 | Micro-F1 | ROC-AUC | Paper |
|---|---|---|---|---|---|---|---|---|---|
| 1 | GraphTARIF | 0.9461
±
0.0017 | — | — | — | — | — | — | |
| 2 | GCN | 0.9412
±
0.0008 | — | — | — | — | — | — | |
| 3 | GAT | 0.9398
±
0.0022 | — | — | — | — | — | — | |
| 4 | GCN+TS | 0.9398
±
0.0026 | — | — | — | — | — | — | |
| 5 | GAT+T | 0.9386
±
0.0013 | — | — | — | — | — | — | |
| 6 | Polynormer | 0.9378
±
0.0010 | — | — | — | — | — | — | |
| 7 | Polynormer-r | 0.9368
±
0.0021 | — | — | — | — | — | — | |
| 8 | SAGE | 0.9351
±
0.0006 | — | — | — | — | — | — | |
| 9 | SAGE+T | 0.9343
±
0.0011 | — | — | — | — | — | — | |
| 10 | Polynormer-v2 | 0.9325
±
0.0022 | — | — | — | — | — | — | |
| 11 | Polynormer | 0.9318
±
0.0018 | — | — | — | — | — | — | |
| 12 | Polynormer-v1 | 0.9318
±
0.0018 | — | — | — | — | — | — | |
| 13 | OGFormer | 0.9290
±
0.0030 | — | — | — | — | — | — | |
| 14 | TabPFN-GN | 0.9271
±
0.0003 | — | — | — | — | — | — | |
| 15 | CNA (TransformerConv) | 0.9268
±
0.0027 | — | — | — | — | — | — | |
| 16 | EGTAS | 0.9253
±
0.0066 | — | — | — | — | — | — | |
| 17 | VecFormer | 0.9251
±
0.0054 | — | — | — | — | — | — | |
| 18 | BGAE + Att | 0.9244 | — | — | — | — | — | — | |
| 19 | SGFormer | 0.9242
±
0.0066 | — | — | — | — | — | — | |
| 20 | LaplaceGNN | 0.9230
±
0.0023 | — | — | — | — | — | — | |
| 21 | GOAT | 0.9229
±
0.0037 | — | — | — | — | — | — | |
| 22 | WaveGC | 0.9226
±
0.0018 | — | — | — | — | — | — | |
| 23 | Matrix-valued | 0.9226 | — | — | — | — | — | — | |
| 24 | BGAE | 0.9224 | — | — | — | — | — | — | |
| 25 | LECT_1+LECT_2 | 0.9220
±
0.0060 | — | — | — | — | — | — | |
| 26 | WaveNet | 0.9206
±
0.0033 | — | — | — | — | — | — | |
| 27 | OrderedGNN | 0.9203
±
0.0013 | — | — | — | — | — | — | |
| 28 | DIFFormer | 0.9199
±
0.0076 | — | — | — | — | — | — | |
| 29 | Polynormer | 0.9192
±
0.0025 | — | — | — | — | — | — | |
| 30 | GCNII-GAT | 0.9191
±
0.0011 | — | — | — | — | — | — | |
| 31 | OEPG | 0.9190
±
0.0050 | — | — | — | — | — | — | |
| 32 | GGCN | 0.9181
±
0.0020 | — | — | — | — | — | — | |
| 33 | GIRL | 0.9180
±
0.0000 | — | — | — | — | — | — | |
| 34 | JK-GAT | 0.9180
±
0.0023 | — | — | — | — | — | — | |
| 35 | NAGphormer-LGTL | 0.9178
±
0.0012 | — | — | — | — | — | — | |
| 36 | AdNGCL | 0.9176
±
0.0029 | — | — | — | — | — | — | |
| 37 | SignGT | 0.9171
±
0.0044 | — | — | — | — | — | — | |
| 38 | NTFormer | 0.9169
±
0.0032 | — | — | — | — | — | — | |
| 39 | NAGphormer | 0.9169
±
0.0030 | — | — | — | — | — | — | |
| 40 | DEFT | 0.9163
±
0.0052 | — | — | — | — | — | — | |
| 41 | FusedGAT+GPL | 0.9161
±
0.0031 | — | — | — | — | — | — | |
| 42 | EXPHORMER + GCN | 0.9159
±
0.0031 | — | — | — | — | — | — | |
| 43 | CGT | 0.9150
±
0.0060 | — | — | — | — | — | — | |
| 44 | Exphormer | 0.9147
±
0.0017 | — | — | — | — | — | — | |
| 45 | F2GNN | 0.9142
±
0.0026 | — | — | — | — | — | — | |
| 46 | NAGphormer | 0.9122
±
0.0014 | — | — | — | — | — | — | |
| 47 | NAG | 0.9122
±
0.0014 | — | — | — | — | — | — | |
| 48 | GraphSAGE | 0.9120
±
0.0029 | — | — | — | — | — | — | |
| 49 | GraphGPS | 0.9119
±
0.0054 | — | — | — | — | — | — | |
| 50 | VCR-Graphormer-LGTL | 0.9113
±
0.0014 | — | — | — | — | — | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time