TU Dortmund
NCI1
Structure
4,110Graphs
30Nodes
32Edges
2Classes
37Node Feature Dim
Coverage
713Results
1Variants
131Papers
- 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
- USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation
- MAGPrompt: Message-Adaptive Graph Prompt Tuning for Graph Neural Networks MAGPrompt: Message-Adaptive Graph Prompt Tuning for Graph Neural Networks
- CORE: Contrastive Masked Feature Reconstruction on Graphs CORE: Contrastive Masked Feature Reconstruction on Graphs
- Torch Geometric Pool: the Pytorch library for pooling in Graph Neural Networks Torch Geometric Pool: the Pytorch library for pooling in Graph Neural Networks
- NeurIPS 2025: Enhancing Graph Classification Robustness with Singular Pooling Enhancing Graph Classification Robustness with Singular Pooling NeurIPS 2025
- 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
- Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
- Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
- NeurIPS 2025: Graph Persistence goes Spectral Graph Persistence goes Spectral NeurIPS 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
- ICML 2025: Learnable Kernel Density Estimation for Graphs Learnable Kernel Density Estimation for Graphs ICML 2025
- ICML 2025: Learn Beneficial Noise as Graph Augmentation Learn Beneficial Noise as Graph Augmentation ICML 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
- InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets
- TMLR 2025: Towards Efficient Training of Graph Neural Networks: A Multiscale Approach Towards Efficient Training of Graph Neural Networks: A Multiscale Approach TMLR 2025
- WWW 2025: Graph Self-Supervised Learning with Learnable Structural and Positional Encodings Graph Self-Supervised Learning with Learnable Structural and Positional Encodings WWW 2025
- AAAI 2024: Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck 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
- IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
- Range-aware Positional Encoding via High-order Pretraining: Theory and Practice Range-aware Positional Encoding via High-order Pretraining: Theory and Practice
- 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
- NeurIPS 2024: Non-convolutional Graph Neural Networks Non-convolutional Graph Neural Networks 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
- NeurIPS 2024: DiGRAF: Diffeomorphic Graph-Adaptive Activation Function DiGRAF: Diffeomorphic Graph-Adaptive Activation Function 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
- 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
- NeurIPS 2024: GRANOLA: Adaptive Normalization for Graph Neural Networks GRANOLA: Adaptive Normalization for Graph Neural Networks NeurIPS 2024
- AAAI 2024: TopoGCL: Topological Graph Contrastive Learning TopoGCL: Topological Graph Contrastive Learning AAAI 2024
- Graph Learning with Distributional Edge Layouts Graph Learning with Distributional Edge Layouts
- ICLR 2024: Graph Parsing Networks Graph Parsing Networks ICLR 2024
- UGMAE: A Unified Framework for Graph Masked Autoencoders UGMAE: A Unified Framework for Graph Masked Autoencoders
- Two Trades are not Baffled: Condensing Graph via Crafting Rational Gradient Matching Two Trades are not Baffled: Condensing Graph via Crafting Rational Gradient Matching
- GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling
- Unifying Graph Contrastive Learning via Graph Message Augmentation Unifying Graph Contrastive Learning via Graph Message Augmentation
- 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
- Graph Self-Contrast Representation Learning Graph Self-Contrast Representation Learning
- 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
- On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
- ICML 2023: Graph Mixup with Soft Alignments Graph Mixup with Soft Alignments ICML 2023
- Subgraph Networks Based Contrastive Learning Subgraph Networks Based Contrastive Learning
- NeurIPS 2023: Fine-grained Expressivity of Graph Neural Networks Fine-grained Expressivity of Graph Neural Networks NeurIPS 2023
- ICML 2023: Randomized Schur Complement Views for Graph Contrastive Learning Randomized Schur Complement Views for Graph Contrastive Learning ICML 2023
- GIMM: InfoMin-Max for Automated Graph Contrastive Learning GIMM: InfoMin-Max for Automated Graph Contrastive Learning
- AAAI 2023: Union Subgraph Neural Networks Union Subgraph Neural Networks AAAI 2023
- ICML 2023: Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering ICML 2023
- 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
- 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
- Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network Deep Graph-Level Clustering Using Pseudo-Label-Guided Mutual Information Maximization Network
- 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
- ICML 2022: Total Variation Graph Neural Networks Total Variation Graph Neural Networks ICML 2022
- Global Counterfactual Explainer for Graph Neural Networks Global Counterfactual Explainer for Graph Neural Networks
- Cell Attention Networks Cell Attention Networks
- Graph Contrastive Learning with Personalized Augmentation Graph Contrastive Learning with Personalized Augmentation
- Higher-order Clustering and Pooling for Graph Neural Networks Higher-order Clustering and Pooling for Graph Neural Networks
- ICML 2022: Optimization-Induced Graph Implicit Nonlinear Diffusion Optimization-Induced Graph Implicit Nonlinear Diffusion ICML 2022
- 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
- NeurIPS 2022: Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries NeurIPS 2022
- ICML 2022: Let Invariant Rationale Discovery Inspire Graph Contrastive Learning Let Invariant Rationale Discovery Inspire Graph Contrastive Learning ICML 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
- SoftEdge: Regularizing Graph Classification with Random Soft Edges SoftEdge: Regularizing Graph Classification with Random Soft Edges
- OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks
- 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
- Graph Masked Autoencoders with Transformers Graph Masked Autoencoders with Transformers
- WWW 2022: Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning WWW 2022
- ICML 2022: Self-Supervised Representation Learning via Latent Graph Prediction Self-Supervised Representation Learning via Latent Graph Prediction ICML 2022
- 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
- TMLR 2022: How Expressive are Transformers in Spectral Domain for Graphs? How Expressive are Transformers in Spectral Domain for Graphs? TMLR 2022
- NeurIPS 2022: Representing Long-Range Context for Graph Neural Networks with Global Attention Representing Long-Range Context for Graph Neural Networks with Global Attention NeurIPS 2022
- AAAI 2022: KerGNNs: Interpretable Graph Neural Networks with Graph Kernels KerGNNs: Interpretable Graph Neural Networks with Graph Kernels AAAI 2022
- ICML 2021: A New Perspective on the Effects of Spectrum in Graph Neural Networks A New Perspective on the Effects of Spectrum in Graph Neural Networks ICML 2021
- WWW 2021: Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices WWW 2021
- NeurIPS 2021: InfoGCL: Information-Aware Graph Contrastive Learning InfoGCL: Information-Aware Graph Contrastive Learning NeurIPS 2021
- 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
- NeurIPS 2021: Federated Graph Classification over Non-IID Graphs Federated Graph Classification over Non-IID Graphs NeurIPS 2021
- NeurIPS 2021: Weisfeiler and Lehman Go Cellular: CW Networks Weisfeiler and Lehman Go Cellular: CW Networks NeurIPS 2021
- Prototypical Graph Contrastive Learning Prototypical Graph Contrastive Learning
- GraphiT: Encoding Graph Structure in Transformers GraphiT: Encoding Graph Structure in Transformers
- ICML 2021: Graph Contrastive Learning Automated Graph Contrastive Learning Automated ICML 2021
- NeurIPS 2021: Adversarial Graph Augmentation to Improve Graph Contrastive Learning Adversarial Graph Augmentation to Improve Graph Contrastive Learning NeurIPS 2021
- ICML 2021: Generative Causal Explanations for Graph Neural Networks Generative Causal Explanations for Graph Neural Networks ICML 2021
- ICML 2021: Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks ICML 2021
- WWW 2021: CogDL: A Comprehensive Library for Graph Deep Learning CogDL: A Comprehensive Library for Graph Deep Learning WWW 2021
- ICLR 2021: Topological Graph Neural Networks Topological Graph Neural Networks ICLR 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
- NeurIPS 2020: Rethinking pooling in graph neural networks Rethinking pooling in graph neural networks NeurIPS 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
- NeurIPS 2020: Implicit Graph Neural Networks Implicit Graph Neural Networks NeurIPS 2020
- AAAI 2020: Contrastive Self-supervised Learning for Graph Classification Contrastive Self-supervised Learning for Graph Classification AAAI 2020
- ICML 2020: GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training ICML 2020
- NeurIPS 2020: Natural Graph Networks Natural Graph Networks NeurIPS 2020
- Graph Neural Networks in TensorFlow and Keras with Spektral Graph Neural Networks in TensorFlow and Keras with Spektral
- ICLR 2020: Wasserstein Embedding for Graph Learning Wasserstein Embedding for Graph Learning ICLR 2020
- ICLR 2020: On the Bottleneck of Graph Neural Networks and its Practical Implications On the Bottleneck of Graph Neural Networks and its Practical Implications 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
- NeurIPS 2019: Wasserstein Weisfeiler-Lehman Graph Kernels Wasserstein Weisfeiler-Lehman Graph Kernels NeurIPS 2019
- 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: Provably Powerful Graph Networks Provably Powerful Graph Networks NeurIPS 2019
- KDD 2019: Graph Convolutional Networks with EigenPooling Graph Convolutional Networks with EigenPooling KDD 2019
- Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
- ICML 2018: Graph Capsule Convolutional Neural Networks Graph Capsule Convolutional Neural Networks ICML 2018
- ICLR 2018: Covariant Compositional Networks for Learning Graphs Covariant Compositional Networks for Learning Graphs ICLR 2018
- Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on 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
- ICML 2016: Learning Convolutional Neural Networks for Graphs Learning Convolutional Neural Networks for Graphs ICML 2016
- ICML 2016: Discriminative Embeddings of Latent Variable Models for Structured Data Discriminative Embeddings of Latent Variable Models for Structured Data ICML 2016
- NeurIPS 2016: The Multiscale Laplacian Graph Kernel The Multiscale Laplacian Graph Kernel NeurIPS 2016
- Propagation Kernels Propagation Kernels
2-class graph classification Graph classification of chemical compounds screened for anti-cancer activity. Uses the released node/edge labels or attributes; several social datasets are featureless. Evaluated by Accuracy.
Dataset details
Task
Graph classification of chemical compounds screened for anti-cancer activity. The primary catalog metric is Accuracy.
Data
Graph classification of chemical compounds screened for anti-cancer activity. The cataloged artifact reports 4,110 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 | Paper |
|---|---|---|---|---|
| 1 | WL-MLP | 0.9954 | — | |
| 2 | GIN | 0.9934 | — | |
| 3 | GCN | 0.9727 | — | |
| 4 | GAT | 0.9623 | — | |
| 5 | DiffPool | 0.9210
±
0.0260 | — | |
| 6 | MSH-GNN | 0.8860
±
0.0050 | — | |
| 7 | WL-PM | 0.8640
±
0.0020 | — | |
| 8 | WL-OA | 0.8610
±
0.0020 | — | |
| 9 | WLK | 0.8600
±
0.0180 | — | |
| 10 | WL subtree | 0.8600
±
0.0180 | — | |
| 11 | WL kernel | 0.8600
±
0.0180 | — | |
| 12 | WL | 0.8590
±
0.0010 | — | |
| 13 | CoCN | 0.8583
±
0.0086 | — | |
| 14 | Weisfeiler--Lehman-inspired embedding scheme | 0.8575
±
0.0025 | — | |
| 15 | WWL | 0.8570
±
0.0080 | — | |
| 16 | Q-WL-RF | 0.8570
±
0.0150 | — | |
| 17 | WL | 0.8560
±
0.0120 | — | |
| 18 | GSO | 0.8547
±
0.0138 | — | |
| 19 | P-WL | 0.8540
±
0.0010 | — | |
| 20 | PIN | 0.8510
±
0.0150 | — | |
| 21 | GIN-AK+ | 0.8500
±
0.0200 | — | |
| 22 | GNN-AK+ | 0.8500
±
0.0200 | — | |
| 23 | Norm-GN | 0.8487
±
0.0168 | — | |
| 24 | CapsNets | 0.8487
±
0.0168 | — | |
| 25 | OEPG | 0.8480
±
0.0040 | — | |
| 26 | Spec-GN | 0.8479
±
0.0163 | — | |
| 27 | WL | 0.8476
±
0.0032 | — | |
| 28 | WL-Edge | 0.8465
±
0.0025 | — | |
| 29 | CAN | 0.8450
±
0.0160 | — | |
| 30 | GIN + HOPE-WavePE | 0.8450
±
0.0200 | — | |
| 31 | RetGK | 0.8450
±
0.0020 | — | |
| 32 | propagation kernels | 0.8450
±
0.0010 | — | |
| 33 | PK | 0.8450
±
0.0010 | — | |
| 34 | SUGAR | 0.8439
±
0.0163 | — | |
| 35 | GPS(DEL-F) | 0.8436
±
0.0015 | — | |
| 36 | GraphGPS | 0.8421
±
0.0225 | — | |
| 37 | SUN (GIN) (NM) | 0.8420
±
0.0150 | — | |
| 38 | GNTK | 0.8420
±
0.0150 | — | |
| 39 | SUN (GIN) (EGO+) | 0.8420
±
0.0150 | — | |
| 40 | GIN + Granola | 0.8400
±
0.0170 | — | |
| 41 | SoftEdge | 0.8390
±
0.0010 | — | |
| 42 | ifMixup | 0.8390
±
0.0040 | — | |
| 43 | VEPM | 0.8390
±
0.0180 | — | |
| 44 | ECC | 0.8380 | — | |
| 45 | DS-GNN (GIN) (ND) | 0.8380
±
0.0240 | — | |
| 46 | structure2vec | 0.8372 | — | |
| 47 | DE-LBP | 0.8372 | — | |
| 48 | DSS-GNN (GIN) (EGO+) | 0.8370
±
0.0180 | — | |
| 49 | GCN | 0.8365
±
0.0169 | — | |
| 50 | GIN + Median | 0.8360
±
0.0190 | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time