TU Dortmund
COLLAB
Structure
5,000Graphs
75Nodes
2,458Edges
3Classes
Coverage
479Results
1Variants
91Papers
- CORE: Contrastive Masked Feature Reconstruction on Graphs CORE: Contrastive Masked Feature Reconstruction on Graphs
- ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity
- SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network
- IJCAI 2025: Explainable Graph Representation Learning via Graph Pattern Analysis Explainable Graph Representation Learning via Graph Pattern Analysis IJCAI 2025
- Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks
- Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
- Model-Driven Graph Contrastive Learning Model-Driven Graph Contrastive Learning
- ICML 2025: Learnable Kernel Density Estimation for Graphs Learnable Kernel Density Estimation for Graphs ICML 2025
- Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing
- Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors
- WWW 2025: Graph Self-Supervised Learning with Learnable Structural and Positional Encodings Graph Self-Supervised Learning with Learnable Structural and Positional Encodings WWW 2025
- ICML 2025: Beyond Message Passing: Neural Graph Pattern Machine Beyond Message Passing: Neural Graph Pattern Machine ICML 2025
- AAAI 2024: Graph Structure Refinement with Energy-based Contrastive Learning Graph Structure Refinement with Energy-based Contrastive Learning AAAI 2024
- KDD 2024: Revisiting Graph Autoencoders as Implicit Contrastive Learners Revisiting Graph Autoencoders as Implicit Contrastive Learners KDD 2024
- SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
- NeurIPS 2024: Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention NeurIPS 2024
- Disentangled Generative Graph Representation Learning Disentangled Generative Graph Representation Learning
- NeurIPS 2024: Graph Classification via Reference Distribution Learning: Theory and Practice Graph Classification via Reference Distribution Learning: Theory and Practice NeurIPS 2024
- Scalable Graph Compressed Convolutions Scalable Graph Compressed Convolutions
- TMLR 2024: Graph Pooling via Ricci Flow Graph Pooling via Ricci Flow TMLR 2024
- Scalable Expressiveness through Preprocessed Graph Perturbations Scalable Expressiveness through Preprocessed Graph Perturbations
- NeurIPS 2024: ProG: A Graph Prompt Learning Benchmark ProG: A Graph Prompt Learning Benchmark NeurIPS 2024
- NeurIPS 2024: Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models NeurIPS 2024
- Dual-perspective Cross Contrastive Learning in Graph Transformers Dual-perspective Cross Contrastive Learning in Graph Transformers
- 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: Community-Invariant Graph Contrastive Learning Community-Invariant Graph Contrastive Learning ICML 2024
- IJCAI 2024: Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders IJCAI 2024
- Training MLPs on Graphs without Supervision Training MLPs on Graphs without Supervision
- UGMAE: A Unified Framework for Graph Masked Autoencoders UGMAE: A Unified Framework for Graph Masked Autoencoders
- AAAI 2024: Rethinking Graph Masked Autoencoders through Alignment and Uniformity Rethinking Graph Masked Autoencoders through Alignment and Uniformity AAAI 2024
- WWW 2024: Graph Contrastive Learning with Cohesive Subgraph Awareness Graph Contrastive Learning with Cohesive Subgraph Awareness WWW 2024
- Unifying Graph Contrastive Learning via Graph Message Augmentation Unifying Graph Contrastive Learning via Graph Message Augmentation
- NeurIPS 2023: Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks NeurIPS 2023
- Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
- SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
- ICLR 2023: Locality-Aware Graph Rewiring in GNNs Locality-Aware Graph Rewiring in GNNs ICLR 2023
- Graph Self-Contrast Representation Learning Graph Self-Contrast Representation Learning
- ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
- GIMM: InfoMin-Max for Automated Graph Contrastive Learning GIMM: InfoMin-Max for Automated Graph Contrastive Learning
- NeurIPS 2023: The expressive power of pooling in Graph Neural Networks The expressive power of pooling in Graph Neural Networks NeurIPS 2023
- SGL-PT: A Strong Graph Learner with Graph Prompt Tuning SGL-PT: A Strong Graph Learner with Graph Prompt Tuning
- Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
- ICML 2022: Total Variation Graph Neural Networks Total Variation Graph Neural Networks ICML 2022
- ICLR 2022: FoSR: First-order Spectral Rewiring for addressing Oversquashing in GNNs FoSR: First-order Spectral Rewiring for addressing Oversquashing in GNNs ICLR 2022
- ICLR 2022: Spectral Augmentation for Self-Supervised Learning on Graphs Spectral Augmentation for Self-Supervised Learning on Graphs ICLR 2022
- Graph Contrastive Learning with Personalized Augmentation Graph Contrastive Learning with Personalized Augmentation
- AAAI 2022: Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning Wiener Graph Deconvolutional Network Improves Graph Self-Supervised Learning AAAI 2022
- ICML 2022: Structural Entropy Guided Graph Hierarchical Pooling Structural Entropy Guided Graph Hierarchical Pooling ICML 2022
- ICML 2022: Let Invariant Rationale Discovery Inspire Graph Contrastive Learning Let Invariant Rationale Discovery Inspire Graph Contrastive Learning ICML 2022
- LoG 2022: DiffWire: Inductive Graph Rewiring via the Lovász Bound DiffWire: Inductive Graph Rewiring via the Lovász Bound LoG 2022
- TMLR 2022: Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm TMLR 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
- ICML 2022: Going Deeper into Permutation-Sensitive Graph Neural Networks Going Deeper into Permutation-Sensitive Graph Neural Networks ICML 2022
- KDD 2022: GraphMAE: Self-Supervised Masked Graph Autoencoders GraphMAE: Self-Supervised Masked Graph Autoencoders KDD 2022
- NeurIPS 2022: Label-invariant Augmentation for Semi-Supervised Graph Classification Label-invariant Augmentation for Semi-Supervised Graph Classification NeurIPS 2022
- Embedding Graphs on Grassmann Manifold Embedding Graphs on Grassmann Manifold
- Incorporating Heterophily into Graph Neural Networks for Graph Classification Incorporating Heterophily into Graph Neural Networks for Graph Classification
- ICLR 2022: Automated Data Augmentations for Graph Classification Automated Data Augmentations for Graph Classification ICLR 2022
- 1-WL Expressiveness Is (Almost) All You Need 1-WL Expressiveness Is (Almost) All You Need
- ICML 2022: Self-Supervised Representation Learning via Latent Graph Prediction Self-Supervised Representation Learning via Latent Graph Prediction ICML 2022
- TMLR 2022: Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings TMLR 2022
- Learning Graph Augmentations to Learn Graph Representations Learning Graph Augmentations to Learn Graph Representations
- AAAI 2022: KerGNNs: Interpretable Graph Neural Networks with Graph Kernels KerGNNs: Interpretable Graph Neural Networks with Graph Kernels AAAI 2022
- Improving Subgraph Recognition with Variational Graph Information Bottleneck Improving Subgraph Recognition with Variational Graph Information Bottleneck
- AAAI 2021: Graph Structure Learning with Variational Information Bottleneck Graph Structure Learning with Variational Information Bottleneck AAAI 2021
- OOD-GNN: Out-of-Distribution Generalized Graph Neural Network OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
- NeurIPS 2021: InfoGCL: Information-Aware Graph Contrastive Learning InfoGCL: Information-Aware Graph Contrastive Learning NeurIPS 2021
- Prototypical Graph Contrastive Learning Prototypical Graph Contrastive Learning
- NeurIPS 2021: Adversarial Graph Augmentation to Improve Graph Contrastive Learning Adversarial Graph Augmentation to Improve Graph Contrastive Learning NeurIPS 2021
- ICML 2021: Graph Contrastive Learning Automated Graph Contrastive Learning Automated ICML 2021
- IJCAI 2021: Graph-Free Knowledge Distillation for Graph Neural Networks Graph-Free Knowledge Distillation for Graph Neural Networks IJCAI 2021
- WWW 2021: CogDL: A Comprehensive Library for Graph Deep Learning CogDL: A Comprehensive Library for Graph Deep Learning WWW 2021
- Graph Self-Supervised Learning: A Survey Graph Self-Supervised Learning: A Survey
- ICLR 2020: On Graph Neural Networks versus Graph-Augmented MLPs On Graph Neural Networks versus Graph-Augmented MLPs ICLR 2020
- NeurIPS 2020: Factorizable Graph Convolutional Networks Factorizable Graph Convolutional Networks NeurIPS 2020
- GraphCrop: Subgraph Cropping for Graph Classification GraphCrop: Subgraph Cropping for Graph Classification
- ICML 2020: GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training ICML 2020
- Quaternion Graph Neural Networks Quaternion Graph Neural Networks
- NeurIPS 2020: Natural Graph Networks Natural Graph Networks NeurIPS 2020
- KDD 2020: GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training KDD 2020
- ICLR 2020: Wasserstein Embedding for Graph Learning Wasserstein Embedding for Graph Learning ICLR 2020
- ICLR 2020: Memory-Based Graph Networks Memory-Based Graph Networks ICLR 2020
- NeurIPS 2019: Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels NeurIPS 2019
- NeurIPS 2019: On the Equivalence between Graph Isomorphism Testing and Function Approximation with GNNs On the Equivalence between Graph Isomorphism Testing and Function Approximation with GNNs NeurIPS 2019
- Edge Contraction Pooling for Graph Neural Networks Edge Contraction Pooling for Graph Neural Networks
- Graph U-Nets Graph U-Nets
- ICLR 2019: Fast Graph Representation Learning with PyTorch Geometric Fast Graph Representation Learning with PyTorch Geometric ICLR 2019
- ICLR 2018: How Powerful are Graph Neural Networks? How Powerful are Graph Neural Networks? ICLR 2018
- ICML 2018: Anonymous Walk Embeddings Anonymous Walk Embeddings ICML 2018
- ICML 2018: Graph Capsule Convolutional Neural Networks Graph Capsule Convolutional Neural Networks ICML 2018
- NeurIPS 2016: On Valid Optimal Assignment Kernels and Applications to Graph Classification On Valid Optimal Assignment Kernels and Applications to Graph Classification NeurIPS 2016
3-class graph classification Graph classification of scientific collaboration networks (3 classes). Uses the released node/edge labels or attributes; several social datasets are featureless. Evaluated by Accuracy.
Dataset details
Task
Graph classification of scientific collaboration networks (3 classes). The primary catalog metric is Accuracy.
Data
Graph classification of scientific collaboration networks (3 classes). The cataloged artifact reports 5,000 graphs, 2 target classes or tasks.
Features
The TU text format may contain discrete node or edge labels and optional continuous attributes. Social-network datasets commonly have no intrinsic node attributes, so papers often add constant or degree features; that choice changes the input.
Splits and evaluation
The catalog records these protocols or variants: 10-fold CV. Evaluation uses Accuracy (higher is better). Exact masks or folds must come from the cited release.
Quirks and pitfalls
TU datasets have no canonical train/test split. Report cross-validation folds, seeds, feature construction, and whether duplicate or isomorphic graphs were removed; published results can otherwise differ for protocol rather than model quality.
Sources
SOTA Progression
new SOTA
matches SOTA at that time
other proposed result
other baseline result
Leaderboard
| # | Model | Accuracy ↑ | AUROC | F1 | Testing Accuracy | equivalence classes | Paper |
|---|---|---|---|---|---|---|---|
| 1 | WL-MLP | 0.9792 | — | — | — | — | |
| 2 | MSH-GNN | 0.9640
±
0.0070 | — | — | — | — | |
| 3 | CoCN | 0.8722
±
0.0013 | — | — | — | — | |
| 4 | N^2 | 0.8670
±
0.0160 | — | — | — | — | |
| 5 | CoCN vanilla | 0.8615
±
0.0010 | — | — | — | — | |
| 6 | graph2vec | 0.8558 | — | — | — | — | |
| 7 | GSN | 0.8550
±
0.0120 | — | — | — | — | |
| 8 | OEPG | 0.8470
±
0.0070 | — | — | — | — | |
| 9 | CORE (GraphMAE2 + CORE) | 0.8430
±
0.0010 | — | — | — | — | |
| 10 | GraphMAE2 | 0.8410
±
0.0030 | — | — | — | — | |
| 11 | PXGL-GNN | 0.8396
±
0.0159 | — | — | — | — | |
| 12 | DiGGR | 0.8376
±
0.0370 | — | — | — | — | |
| 13 | GNTK | 0.8360
±
0.0100 | — | — | — | — | |
| 14 | SE2P-C3 | 0.8350
±
0.0170 | — | — | — | — | |
| 15 | SE2P-C2 | 0.8330
±
0.0210 | — | — | — | — | |
| 16 | Attention | 0.8322
±
0.0030 | — | — | — | — | |
| 17 | ECL-GSR | 0.8300
±
0.0143 | — | — | — | — | |
| 18 | EGG | 0.8294
±
0.0106 | — | — | — | — | |
| 19 | GraphAug | 0.8290
±
0.0020 | — | — | — | — | |
| 20 | SE2P-C4 | 0.8280
±
0.0210 | — | — | — | — | |
| 21 | SAN | 0.8273
±
0.0192 | — | — | — | — | |
| 22 | GraphMAE | 0.8270
±
0.0100 | — | — | — | — | |
| 23 | Empowering GNNs | 0.8250
±
0.0120 | — | — | — | — | |
| 24 | LaplaceGNN | 0.8241
±
0.0042 | — | — | — | — | |
| 25 | SignGT | 0.8240
±
0.0014 | — | — | — | — | |
| 26 | lrGAE 6 | 0.8230
±
0.0070 | — | — | — | — | |
| 27 | lrGAE 7 | 0.8230
±
0.0040 | — | — | — | — | |
| 28 | lrGAE 8 | 0.8220
±
0.0020 | — | — | — | — | |
| 29 | S2GAE | 0.8220
±
0.0040 | — | — | — | — | |
| 30 | JOAOv2 | 0.8216
±
0.0017 | — | — | — | — | |
| 31 | DiffPool | 0.8213
±
0.0043 | — | — | — | — | |
| 32 | DiffPool-Det | 0.8213 | — | — | — | — | Graph U-Nets
no code |
| 33 | MaskGAE | 0.8200
±
0.0050 | — | — | — | — | |
| 34 | CTAug-JOAO | 0.8190
±
0.0053 | — | — | — | — | |
| 35 | SimGRACE | 0.8188
±
0.0023 | — | — | — | — | |
| 36 | Graphormer | 0.8180
±
0.0224 | — | — | — | — | |
| 37 | WGDN | 0.8176
±
0.0024 | — | — | — | — | |
| 38 | SEP-G ICE | 0.8176
±
0.0081 | — | — | — | — | |
| 39 | SAGPool | 0.8176
±
0.0157 | — | — | — | — | |
| 40 | CTAug-GraphCL | 0.8172
±
0.0026 | — | — | — | — | |
| 41 | GraphMAE2 | 0.8170
±
0.0080 | — | — | — | — | |
| 42 | GCKM | 0.8170
±
0.0150 | — | — | — | — | |
| 43 | Teacher (100% training data) | 0.8170 | — | — | — | — | |
| 44 | UGMAE | 0.8166
±
0.0012 | — | — | — | — | |
| 45 | Uniform PerturbEdge | 0.8160
±
0.0030 | — | — | — | — | |
| 46 | GLA | 0.8154
±
0.0014 | — | — | — | — | |
| 47 | BGRL | 0.8150
±
0.0070 | — | — | — | — | |
| 48 | GFN | 0.8150
±
0.0242 | — | — | — | — | |
| 49 | WL-PM | 0.8150
±
0.0050 | — | — | — | — | |
| 50 | Uniform MaskNF | 0.8150
±
0.0020 | — | — | — | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time