TU Dortmund
MUTAG
Structure
188Graphs
18Nodes
20Edges
2Classes
7Node Feature Dim
4Edge Feature Dim
Coverage
917Results
1Variants
191Papers
- ICML 2026: RADE: Random Add-Drop Edge as a Regularizer RADE: Random Add-Drop Edge as a Regularizer ICML 2026
- TMLR 2026: AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks TMLR 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
- Position: Don't be Afraid of Over-Smoothing And Over-Squashing Position: Don't be Afraid of Over-Smoothing And Over-Squashing
- 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
- Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
- 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
- Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
- Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies Towards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
- Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization Graph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
- Exact Verification of Graph Neural Networks with Incremental Constraint Solving Exact Verification of Graph Neural Networks with Incremental Constraint Solving
- Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
- ICML 2025: Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification ICML 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: 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
- Soft causal learning for generalized molecule property prediction: An environment modeling perspective Soft causal learning for generalized molecule property prediction: An environment modeling perspective
- Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors
- InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets InfoGain Wavelets: Furthering the Design of Graph Diffusion Wavelets
- KDD 2025: Depth-Adaptive Graph Neural Networks via Learnable Bakry-Émery Curvature Depth-Adaptive Graph Neural Networks via Learnable Bakry-Émery Curvature KDD 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
- ICML 2025: Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective ICML 2025
- WWW 2025: DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach DAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach WWW 2025
- AAAI 2024: Graph Structure Refinement with Energy-based Contrastive Learning Graph Structure Refinement with Energy-based Contrastive Learning AAAI 2024
- SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
- KDD 2024: Revisiting Graph Autoencoders as Implicit Contrastive Learners Revisiting Graph Autoencoders as Implicit Contrastive Learners KDD 2024
- 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: TopER: Topological Embeddings in Graph Representation Learning TopER: Topological Embeddings in Graph Representation Learning NeurIPS 2024
- 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
- 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
- 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
- ICML 2024: How Interpretable Are Interpretable Graph Neural Networks? How Interpretable Are Interpretable Graph Neural Networks? ICML 2024
- NeurIPS 2024: ProG: A Graph Prompt Learning Benchmark ProG: A Graph Prompt Learning Benchmark NeurIPS 2024
- ICLR 2024: Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs ICLR 2024
- Global-Local Graph Neural Networks for Node-Classification Global-Local Graph Neural Networks for Node-Classification
- 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
- 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
- NeurIPS 2024: Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision NeurIPS 2024
- Graph Learning with Distributional Edge Layouts Graph Learning with Distributional Edge Layouts
- Smoothed Graph Contrastive Learning via Seamless Proximity Integration Smoothed Graph Contrastive Learning via Seamless Proximity Integration
- 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
- ICML 2024: --Leman at the margin: When more expressivity matters --Leman at the margin: When more expressivity matters ICML 2024
- AAAI 2024: Rethinking Graph Masked Autoencoders through Alignment and Uniformity Rethinking Graph Masked Autoencoders through Alignment and Uniformity AAAI 2024
- Two Trades are not Baffled: Condensing Graph via Crafting Rational Gradient Matching Two Trades are not Baffled: Condensing Graph via Crafting Rational Gradient Matching
- PAC Learnability under Explanation-Preserving Graph Perturbations PAC Learnability under Explanation-Preserving Graph Perturbations
- 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
- 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
- Generative Explanations for Graph Neural Network: Methods and Evaluations Generative Explanations for Graph Neural Network: Methods and Evaluations
- NeurIPS 2023: Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive Learning NeurIPS 2023
- NeurIPS 2023: Interpretable Prototype-based Graph Information Bottleneck Interpretable Prototype-based Graph Information Bottleneck NeurIPS 2023
- BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
- 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: Equivariant Matrix Function Neural Networks Equivariant Matrix Function Neural Networks ICLR 2023
- ICLR 2023: GnnX-Bench: Unravelling the Utility of Perturbation-based Gnn Explainers through In-depth Benchmarking GnnX-Bench: Unravelling the Utility of Perturbation-based Gnn Explainers through In-depth Benchmarking ICLR 2023
- ICLR 2023: Locality-Aware Graph Rewiring in GNNs Locality-Aware Graph Rewiring in GNNs ICLR 2023
- TMLR 2023: Graph-level Representation Learning with Joint-Embedding Predictive Architectures Graph-level Representation Learning with Joint-Embedding Predictive Architectures TMLR 2023
- Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature
- Graph Self-Contrast Representation Learning Graph Self-Contrast Representation Learning
- 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
- 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
- Efficient GNN Explanation via Learning Removal-based Attribution Efficient GNN Explanation via Learning Removal-based Attribution
- Subgraph Networks Based Contrastive Learning Subgraph Networks Based Contrastive Learning
- ICML 2023: Randomized Schur Complement Views for Graph Contrastive Learning Randomized Schur Complement Views for Graph Contrastive Learning ICML 2023
- NeurIPS 2023: Fine-grained Expressivity of Graph Neural Networks Fine-grained Expressivity of Graph Neural Networks NeurIPS 2023
- KDD 2023: Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization KDD 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
- 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
- NeurIPS 2023: The expressive power of pooling in Graph Neural Networks The expressive power of pooling in Graph Neural Networks NeurIPS 2023
- Mind the Label Shift of Augmentation-based Graph OOD Generalization Mind the Label Shift of Augmentation-based Graph OOD Generalization
- 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
- 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: 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
- NeurIPS 2022: Geodesic Graph Neural Network for Efficient Graph Representation Learning Geodesic Graph Neural Network for Efficient Graph Representation Learning NeurIPS 2022
- NeurIPS 2022: Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks NeurIPS 2022
- L2XGNN: Learning to Explain Graph Neural Networks L2XGNN: Learning to Explain Graph Neural Networks
- Cell Attention Networks Cell Attention Networks
- ICLR 2022: GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks ICLR 2022
- 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
- ICML 2022: Structural Entropy Guided Graph Hierarchical Pooling Structural Entropy Guided Graph Hierarchical Pooling ICML 2022
- ICLR 2022: Agent-based Graph Neural Networks Agent-based Graph Neural Networks ICLR 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
- Asynchronous Neural Networks for Learning in Graphs Asynchronous Neural Networks for Learning in Graphs
- KDD 2022: GraphMAE: Self-Supervised Masked Graph Autoencoders GraphMAE: Self-Supervised Masked Graph Autoencoders KDD 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
- Supervised Contrastive Learning with Structure Inference for Graph Classification Supervised Contrastive Learning with Structure Inference for Graph Classification
- 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
- 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
- Learning Graph Augmentations to Learn Graph Representations Learning Graph Augmentations to Learn Graph Representations
- TMLR 2022: How Expressive are Transformers in Spectral Domain for Graphs? How Expressive are Transformers in Spectral Domain for Graphs? 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: ProtGNN: Towards Self-Explaining Graph Neural Networks ProtGNN: Towards Self-Explaining Graph Neural Networks AAAI 2021
- NeurIPS 2021: AutoGEL: An Automated Graph Neural Network with Explicit Link Information AutoGEL: An Automated Graph Neural Network with Explicit Link Information NeurIPS 2021
- NeurIPS 2021: DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks NeurIPS 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
- KDD 2021: Heterogeneous Graph Benchmarks Heterogeneous Graph Benchmarks KDD 2021
- On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
- 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
- Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
- GraphiT: Encoding Graph Structure in Transformers GraphiT: Encoding Graph Structure in Transformers
- ICML 2021: Graph Contrastive Learning Automated Graph Contrastive Learning Automated ICML 2021
- ICML 2021: Breaking the Limits of Message Passing Graph Neural Networks Breaking the Limits of Message Passing Graph Neural Networks ICML 2021
- KDD 2021: MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph MoCL: Data-driven Molecular Fingerprint via Knowledge-aware Contrastive Learning from Molecular Graph KDD 2021
- IJCAI 2021: Graph-Free Knowledge Distillation for Graph Neural Networks Graph-Free Knowledge Distillation for Graph Neural Networks IJCAI 2021
- GraphSVX: Shapley Value Explanations for Graph Neural Networks GraphSVX: Shapley Value Explanations for Graph Neural Networks
- ICML 2021: Generative Causal Explanations for Graph Neural Networks Generative Causal Explanations for Graph Neural Networks ICML 2021
- ICLR 2021: AutoGL: A Library for Automated Graph Learning AutoGL: A Library for Automated Graph Learning ICLR 2021
- Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization
- Recognizing Predictive Substructures with Subgraph Information Bottleneck Recognizing Predictive Substructures with Subgraph Information Bottleneck
- 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
- 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
- JMLR 2020: Decimated Framelet System on Graphs and Fast $ $-Framelet Transforms Decimated Framelet System on Graphs and Fast $ $-Framelet Transforms JMLR 2020
- NeurIPS 2020: Factorizable Graph Convolutional Networks Factorizable Graph Convolutional Networks NeurIPS 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
- NeurIPS 2020: Implicit Graph Neural Networks Implicit Graph Neural Networks NeurIPS 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
- Quaternion Graph Neural Networks Quaternion Graph Neural Networks
- MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
- 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
- ICML 2020: Contrastive Multi-View Representation Learning on Graphs Contrastive Multi-View Representation Learning on Graphs ICML 2020
- ICML 2020: Graph Homomorphism Convolution Graph Homomorphism Convolution ICML 2020
- IJCAI 2019: Coloring graph neural networks for node disambiguation Coloring graph neural networks for node disambiguation IJCAI 2019
- WWW 2019: Universal Graph Transformer Self-Attention Networks Universal Graph Transformer Self-Attention Networks WWW 2019
- 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: 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
- 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
- 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
- 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
- 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
- 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
- Modeling Relational Data with Graph Convolutional Networks Modeling Relational Data with Graph Convolutional Networks
- 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
- 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 mutagenic aromatic and heteroaromatic nitro compounds (2 classes). Uses the released node/edge labels or attributes; several social datasets are featureless. Evaluated by Accuracy.
Dataset details
Task
Graph classification of mutagenic aromatic and heteroaromatic nitro compounds (2 classes). The primary catalog metric is Accuracy.
Data
Graph classification of mutagenic aromatic and heteroaromatic nitro compounds (2 classes). The cataloged artifact reports 188 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 ↑ | AUC | F1 | Instability | Predicted Class Probability | Size | Sparsity | Suffic. | Unfaithfulness | Paper |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | MSH-GNN | 0.9910
±
0.0030 | — | — | — | — | — | — | — | — | |
| 2 | ID-GNN+BEC | 0.9830
±
0.0360 | — | — | — | — | — | — | — | — | |
| 3 | SupCosine | 0.9830
±
0.0250 | — | — | — | — | — | — | — | — | |
| 4 | CauEMO | 0.9692
±
0.0136 | — | — | — | — | — | — | — | — | |
| 5 | GALA | 0.9676
±
0.0170 | — | — | — | — | — | — | — | — | |
| 6 | SUGAR | 0.9674
±
0.0455 | — | — | — | — | — | — | — | — | |
| 7 | GSAT | 0.9637
±
0.0215 | — | — | — | — | — | — | — | — | |
| 8 | novel method to learn motif-level feature embedding | 0.9630
±
0.0260 | — | — | — | — | — | — | — | — | |
| 9 | GIN+BEC | 0.9610
±
0.0360 | — | — | — | — | — | — | — | — | |
| 10 | GraphSNN+BEC | 0.9610
±
0.0250 | — | — | — | — | — | — | — | — | |
| 11 | KP-GNN | 0.9610
±
0.0460 | — | — | — | — | — | — | — | — | |
| 12 | IGM | 0.9604
±
0.0201 | — | — | — | — | — | — | — | — | |
| 13 | NC-GNN+BEC | 0.9600
±
0.0240 | — | — | — | — | — | — | — | — | |
| 14 | SortPool+GPL | 0.9600
±
0.0200 | — | — | — | — | — | — | — | — | |
| 15 | CIGA | 0.9577
±
0.0123 | — | — | — | — | — | — | — | — | |
| 16 | KP-GIN | 0.9560
±
0.0440 | — | — | — | — | — | — | — | — | |
| 17 | OEPG | 0.9530
±
0.0060 | — | — | — | — | — | — | — | — | |
| 18 | SAGNN | 0.9520
±
0.0300 | — | — | — | — | — | — | — | — | |
| 19 | GCN+BEC | 0.9500
±
0.0300 | — | — | — | — | — | — | — | — | |
| 20 | GIN+GPL | 0.9500
±
0.0447 | — | — | — | — | — | — | — | — | |
| 21 | GraphSNN | 0.9470
±
0.0190 | — | — | — | — | — | — | — | — | |
| 22 | RTPool | 0.9470
±
0.0330 | — | — | — | — | — | — | — | — | |
| 23 | IB-Subgraph | 0.9433
±
0.0644 | — | — | — | — | — | — | — | — | |
| 24 | sGIN | 0.9410
±
0.0270 | — | — | — | — | — | — | — | — | |
| 25 | CAN | 0.9410
±
0.0480 | — | — | — | — | — | — | — | — | |
| 26 | GMCL-D | 0.9409
±
0.0628 | — | — | — | — | — | — | — | — | |
| 27 | GNN performance | 0.9400 | — | — | — | — | — | — | — | — | |
| 28 | CLIP | 0.9390
±
0.0400 | — | — | — | — | — | — | — | — | |
| 29 | GraphFM | 0.9380
±
0.0660 | — | — | — | — | — | — | — | — | |
| 30 | LiSA | 0.9370
±
0.0140 | — | — | — | — | — | — | — | — | |
| 31 | GIN + HOPE-WavePE | 0.9360
±
0.0580 | — | — | — | — | — | — | — | — | |
| 32 | AgentNet | 0.9360
±
0.0860 | — | — | — | — | — | — | — | — | |
| 33 | VEPM | 0.9360
±
0.0340 | — | — | — | — | — | — | — | — | |
| 34 | DEL-F | 0.9334
±
0.0068 | — | — | — | — | — | — | — | — | |
| 35 | Wit-TopoPool | 0.9320
±
0.0410 | — | — | — | — | — | — | — | — | |
| 36 | HGCN | 0.9316
±
0.0610 | — | — | — | — | — | — | — | — | |
| 37 | GMCL-P | 0.9310
±
0.0533 | — | — | — | — | — | — | — | — | |
| 38 | GMCL-M | 0.9310
±
0.0630 | — | — | — | — | — | — | — | — | |
| 39 | Message perturb | 0.9310
±
0.0533 | — | — | — | — | — | — | — | — | |
| 40 | CI-GNN | 0.9300
±
0.0300 | — | — | — | — | — | — | — | — | |
| 41 | StructPosGSSL-SA | 0.9300
±
0.0530 | — | — | — | — | — | — | — | — | |
| 42 | LaplaceGNN | 0.9285
±
0.0074 | — | — | — | — | — | — | — | — | |
| 43 | SUN (GIN) (EGO) | 0.9270
±
0.0580 | — | — | — | — | — | — | — | — | |
| 44 | CIN | 0.9270
±
0.0610 | — | — | — | — | — | — | — | — | |
| 45 | SAN | 0.9265
±
0.0153 | — | — | — | — | — | — | — | — | |
| 46 | PSCN | 0.9263 | — | — | — | — | — | — | — | — | |
| 47 | PSCN k=10E | 0.9263
±
0.0421 | — | — | — | — | — | — | — | — | |
| 48 | SoTA | 0.9263
±
0.0258 | — | — | — | — | — | — | — | — | |
| 49 | SAT | 0.9260
±
0.0430 | — | — | — | — | — | — | — | — | |
| 50 | PatchySan | 0.9260
±
0.0420 | — | — | — | — | — | — | — | — |
SOTA performance
set a new SOTA at the time
matched the SOTA at the time