TU Dortmund
PROTEINS
Structure
1,113Graphs
39Nodes
73Edges
2Classes
3Node Feature Dim
Coverage
574Results
1Variants
105Papers
- ICML 2026: RADE: Random Add-Drop Edge as a Regularizer RADE: Random Add-Drop Edge as a Regularizer ICML 2026
- Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables
- TMLR 2026: Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks Poisoning the Inner Prediction Logic of Graph Neural Networks for Clean-Label Backdoor Attacks TMLR 2026
- Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks
- ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity ICEPool: Enhancing Graph Pooling Networks with Inter-cluster Connectivity
- IJCAI 2025: Explainable Graph Representation Learning via Graph Pattern Analysis Explainable Graph Representation Learning via Graph Pattern Analysis IJCAI 2025
- ICLR 2025: Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing ICLR 2025
- Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing
- Fractal Graph Contrastive Learning Fractal Graph Contrastive Learning
- Efficient Mixed Precision Quantization in Graph Neural Networks Efficient Mixed Precision Quantization in Graph Neural Networks
- Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors
- 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
- InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets
- WWW 2025: Graph Self-Supervised Learning with Learnable Structural and Positional Encodings Graph Self-Supervised Learning with Learnable Structural and Positional Encodings WWW 2025
- KDD 2025: GCoT: Chain-of-Thought Prompt Learning for Graphs GCoT: Chain-of-Thought Prompt Learning for Graphs KDD 2025
- SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
- IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
- NeurIPS 2024: Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention NeurIPS 2024
- NeurIPS 2024: Unitary convolutions for learning on graphs and groups Unitary convolutions for learning on graphs and groups NeurIPS 2024
- NeurIPS 2024: TopER: Topological Embeddings in Graph Representation Learning TopER: Topological Embeddings in Graph Representation Learning 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
- 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
- ICLR 2024: The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited ICLR 2024
- TMLR 2024: Graph Pooling via Ricci Flow Graph Pooling via Ricci Flow TMLR 2024
- NeurIPS 2024: DiGRAF: Diffeomorphic Graph-Adaptive Activation Function DiGRAF: Diffeomorphic Graph-Adaptive Activation Function NeurIPS 2024
- ICLR 2024: Graph Neural Networks Gone Hogwild Graph Neural Networks Gone Hogwild ICLR 2024
- Scalable Expressiveness through Preprocessed Graph Perturbations Scalable Expressiveness through Preprocessed Graph Perturbations
- Motif-driven Subgraph Structure Learning for Graph Classification Motif-driven Subgraph Structure Learning for Graph Classification
- 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
- Graph Learning with Distributional Edge Layouts Graph Learning with Distributional Edge Layouts
- GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling
- AAAI 2023: Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns AAAI 2023
- Permutation-Invariant graph partitioning: How graph neural networks capture structural interactions? Permutation-Invariant graph partitioning: How graph neural networks capture structural interactions?
- ICLR 2023: Effective Structural Encodings via Local Curvature Profiles Effective Structural Encodings via Local Curvature Profiles ICLR 2023
- NeurIPS 2023: Interpretable Prototype-based Graph Information Bottleneck Interpretable Prototype-based Graph Information Bottleneck NeurIPS 2023
- ICLR 2023: Equivariant Matrix Function Neural Networks Equivariant Matrix Function Neural Networks ICLR 2023
- TMLR 2023: Graph-level Representation Learning with Joint-Embedding Predictive Architectures Graph-level Representation Learning with Joint-Embedding Predictive Architectures TMLR 2023
- Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability
- The Expressive Power of Graph Neural Networks: A Survey The Expressive Power of Graph Neural Networks: A Survey
- AAAI 2023: Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes AAAI 2023
- ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
- NeurIPS 2023: Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications NeurIPS 2023
- CIN++: Enhancing Topological Message Passing CIN++: Enhancing Topological Message Passing
- NeurIPS 2023: Fine-grained Expressivity of Graph Neural Networks Fine-grained Expressivity of Graph Neural Networks NeurIPS 2023
- AAAI 2023: Union Subgraph Neural Networks Union Subgraph Neural Networks AAAI 2023
- KDD 2023: On Structural Expressive Power of Graph Transformers On Structural Expressive Power of Graph Transformers KDD 2023
- ICML 2023: Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering ICML 2023
- WWW 2023: Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification WWW 2023
- CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis
- JMLR 2023: GNNBenchmark GNNBenchmark JMLR 2023
- ICML 2022: Revisiting Over-smoothing and Over-squashing using Ollivier's Ricci Curvature Revisiting Over-smoothing and Over-squashing using Ollivier's Ricci Curvature ICML 2022
- 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
- NeurIPS 2022: Geodesic Graph Neural Network for Efficient Graph Representation Learning Geodesic Graph Neural Network for Efficient Graph Representation Learning NeurIPS 2022
- ICML 2022: Optimization-Induced Graph Implicit Nonlinear Diffusion Optimization-Induced Graph Implicit Nonlinear Diffusion ICML 2022
- ICML 2022: Structural Entropy Guided Graph Hierarchical Pooling Structural Entropy Guided Graph Hierarchical Pooling ICML 2022
- ICML 2022: Going Deeper into Permutation-Sensitive Graph Neural Networks Going Deeper into Permutation-Sensitive Graph Neural Networks ICML 2022
- Embedding Graphs on Grassmann Manifold Embedding Graphs on Grassmann Manifold
- SoftEdge: Regularizing Graph Classification with Random Soft Edges SoftEdge: Regularizing Graph Classification with Random Soft Edges
- ICLR 2022: Automated Data Augmentations for Graph Classification Automated Data Augmentations for Graph Classification ICLR 2022
- Graph Masked Autoencoders with Transformers Graph Masked Autoencoders with Transformers
- WWW 2022: SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation WWW 2022
- ICML 2022: Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks 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
- Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
- Improving Subgraph Recognition with Variational Graph Information Bottleneck Improving Subgraph Recognition with Variational Graph Information Bottleneck
- AAAI 2021: ifMixup: Interpolating Graph Pair to Regularize Graph Classification ifMixup: Interpolating Graph Pair to Regularize Graph Classification AAAI 2021
- ICLR 2021: From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness ICLR 2021
- AAAI 2021: Orthogonal Graph Neural Networks Orthogonal Graph Neural Networks AAAI 2021
- Pooling Architecture Search for Graph Classification Pooling Architecture Search for Graph Classification
- NeurIPS 2021: Weisfeiler and Lehman Go Cellular: CW Networks Weisfeiler and Lehman Go Cellular: CW Networks NeurIPS 2021
- GraphiT: Encoding Graph Structure in Transformers GraphiT: Encoding Graph Structure in Transformers
- NeurIPS 2021: Adversarial Graph Augmentation to Improve Graph Contrastive Learning Adversarial Graph Augmentation to Improve Graph Contrastive Learning NeurIPS 2021
- ICML 2021: Breaking the Limits of Message Passing Graph Neural Networks Breaking the Limits of Message Passing Graph Neural Networks ICML 2021
- ICLR 2021: AutoGL: A Library for Automated Graph Learning AutoGL: A Library for Automated Graph Learning ICLR 2021
- Recognizing Predictive Substructures with Subgraph Information Bottleneck Recognizing Predictive Substructures with Subgraph Information Bottleneck
- ICML 2021: How Framelets Enhance Graph Neural Networks How Framelets Enhance Graph Neural Networks ICML 2021
- WWW 2021: SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism WWW 2021
- AAAI 2020: Hierarchical Graph Capsule Network Hierarchical Graph Capsule Network AAAI 2020
- ICLR 2020: Graph Information Bottleneck for Subgraph Recognition Graph Information Bottleneck for Subgraph Recognition ICLR 2020
- Multi-grained Semantics-aware Graph Neural Networks Multi-grained Semantics-aware Graph Neural Networks
- GraphCrop: Subgraph Cropping for Graph Classification GraphCrop: Subgraph Cropping for Graph Classification
- Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
- 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
- AAAI 2019: ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations AAAI 2019
- Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling
- Understanding Isomorphism Bias in Graph Data Sets Understanding Isomorphism Bias in Graph Data Sets
- KDD 2019: DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification KDD 2019
- NeurIPS 2019: Wasserstein Weisfeiler-Lehman Graph Kernels Wasserstein Weisfeiler-Lehman 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
- KDD 2019: Graph Convolutional Networks with EigenPooling Graph Convolutional Networks with EigenPooling KDD 2019
- ICLR 2019: Fast Graph Representation Learning with PyTorch Geometric Fast Graph Representation Learning with PyTorch Geometric ICLR 2019
- Graph Neural Networks With Convolutional ARMA Filters Graph Neural Networks With Convolutional ARMA Filters
- ICML 2018: Graph Capsule Convolutional Neural Networks Graph Capsule Convolutional Neural Networks ICML 2018
- graph2vec: Learning Distributed Representations of Graphs graph2vec: Learning Distributed Representations of Graphs
- NeurIPS 2016: On Valid Optimal Assignment Kernels and Applications to Graph Classification On Valid Optimal Assignment Kernels and Applications to Graph Classification NeurIPS 2016
2-class graph classification Graph classification of proteins as enzyme or non-enzyme (2 classes). Uses the released node/edge labels or attributes; several social datasets are featureless. No canonical split; report folds and any added node features. Evaluated by Accuracy.
Dataset details
Task
Graph classification of proteins as enzyme or non-enzyme (2 classes). The primary catalog metric is Accuracy.
Data
Graph classification of proteins as enzyme or non-enzyme (2 classes). The cataloged artifact reports 1,113 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 ↑ | AP | Paper |
|---|---|---|---|---|
| 1 | MSH-GNN | 0.9410
±
0.0330 | — | |
| 2 | GRDL | 0.8260
±
0.0120 | — | |
| 3 | GMN | 0.8225 | — | |
| 4 | GRDL-W | 0.8210
±
0.0090 | — | |
| 5 | CI-GNN | 0.8200
±
0.0200 | — | |
| 6 | DiffPool+GPL | 0.8196
±
0.0286 | — | |
| 7 | DiffPool | 0.8161
±
0.0232 | — | |
| 8 | MemGNN | 0.8135 | — | |
| 9 | SUGAR | 0.8134
±
0.0093 | — | |
| 10 | GRDL-S | 0.8110
±
0.0140 | — | |
| 11 | GIN+GPL | 0.8098
±
0.0208 | — | |
| 12 | VEPM | 0.8050
±
0.0280 | — | |
| 13 | pathGCN | 0.8040
±
0.0420 | — | |
| 14 | KP-GNN | 0.8010
±
0.0430 | — | |
| 15 | GNNExplainer | 0.8000
±
0.0100 | — | |
| 16 | PGExplainer | 0.8000
±
0.0200 | — | |
| 17 | WITTOPOPOOL | 0.8000
±
0.0320 | — | |
| 18 | Wit-TopoPool | 0.8000
±
0.0320 | — | |
| 19 | HM-GNN | 0.7990
±
0.0310 | — | |
| 20 | EGG | 0.7980
±
0.0109 | — | |
| 21 | SAGNN | 0.7980
±
0.0380 | — | |
| 22 | KP-GIN | 0.7950
±
0.0440 | — | |
| 23 | CoCN | 0.7948
±
0.0051 | — | |
| 24 | Graphormer-SPIS | 0.7941
±
0.0146 | — | |
| 25 | GAT (LCP, RW) | 0.7940
±
0.0170 | — | |
| 26 | SIB | 0.7900
±
0.0100 | — | |
| 27 | ORC (us) | 0.7900
±
0.0530 | — | |
| 28 | ASAP | 0.7892
±
0.0145 | — | |
| 29 | StructPool | 0.7884
±
0.0170 | — | |
| 30 | PIN | 0.7880
±
0.0440 | — | |
| 31 | Ortho-g-U-Nets | 0.7878 | — | |
| 32 | Graph Transformer +DEL-F | 0.7876
±
0.0040 | — | |
| 33 | Graph Transformer + DEL-K | 0.7862
±
0.0068 | — | |
| 34 | RF-GCN | 0.7860
±
0.0240 | — | |
| 35 | Graphormer-SPD | 0.7859
±
0.0435 | — | |
| 36 | GPS(DEL-K) | 0.7842
±
0.0059 | — | |
| 37 | GraphSNN | 0.7840
±
0.0270 | — | |
| 38 | DisenSemi | 0.7840
±
0.0050 | — | |
| 39 | IGNN-Solver | 0.7840
±
0.0260 | — | |
| 40 | SAGPool+GPL | 0.7821
±
0.0215 | — | |
| 41 | CAN | 0.7820
±
0.0200 | — | |
| 42 | GAT +DEL-F | 0.7819
±
0.0058 | — | |
| 43 | HC-GAE | 0.7813
±
0.0137 | — | |
| 44 | Graphormer-Neighbor | 0.7812
±
0.0362 | — | |
| 45 | SpeGCL | 0.7805
±
0.0123 | — | |
| 46 | GAT | 0.7800
±
0.0300 | — | |
| 47 | 3WL-GNN | 0.7792
±
0.0209 | — | |
| 48 | DiGRAF | 0.7790
±
0.0340 | — | |
| 49 | MIGNN | 0.7790
±
0.0260 | — | |
| 50 | UFGPool-Sum | 0.7777
±
0.0260 | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time