papers_with_code.md
November 7, 2023 · View on GitHub
| Title | Type | Venue | Code | Year | |
|---|---|---|---|---|---|
| 0 | Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective | ⚔Attack | 📝ICLR | :octocat:Code | 2023 |
| 1 | Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning | ⚔Attack | 📝AAAI | :octocat:Code | 2023 |
| 2 | GUAP: Graph Universal Attack Through Adversarial Patching | ⚔Attack | 📝arXiv | :octocat:Code | 2023 |
| 3 | Node Injection for Class-specific Network Poisoning | ⚔Attack | 📝arXiv | :octocat:Code | 2023 |
| 4 | Unnoticeable Backdoor Attacks on Graph Neural Networks | ⚔Attack | 📝WWW | :octocat:Code | 2023 |
| 5 | Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem | ⚔Attack | 📝WSDM | :octocat:Code | 2022 |
| 6 | Inference Attacks Against Graph Neural Networks | ⚔Attack | 📝USENIX Security | :octocat:Code | 2022 |
| 7 | Model Stealing Attacks Against Inductive Graph Neural Networks | ⚔Attack | 📝IEEE Symposium on Security and Privacy | :octocat:Code | 2022 |
| 8 | Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation | ⚔Attack | 📝WWW | :octocat:Code | 2022 |
| 9 | Neighboring Backdoor Attacks on Graph Convolutional Network | ⚔Attack | 📝arXiv | :octocat:Code | 2022 |
| 10 | Understanding and Improving Graph Injection Attack by Promoting Unnoticeability | ⚔Attack | 📝ICLR | :octocat:Code | 2022 |
| 11 | Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs | ⚔Attack | 📝AAAI | :octocat:Code | 2022 |
| 12 | Black-box Node Injection Attack for Graph Neural Networks | ⚔Attack | 📝arXiv | :octocat:Code | 2022 |
| 13 | Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization | ⚔Attack | 📝Asia CCS | :octocat:Code | 2022 |
| 14 | Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees | ⚔Attack | 📝CVPR | :octocat:Code | 2022 |
| 15 | Transferable Graph Backdoor Attack | ⚔Attack | 📝RAID | :octocat:Code | 2022 |
| 16 | Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors | ⚔Attack | 📝IJCAI | :octocat:Code | 2022 |
| 17 | Are Gradients on Graph Structure Reliable in Gray-box Attacks? | ⚔Attack | 📝CIKM | :octocat:Code | 2022 |
| 18 | BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection | ⚔Attack | 📝ICDM | :octocat:Code | 2022 |
| 19 | Sparse Vicious Attacks on Graph Neural Networks | ⚔Attack | 📝arXiv | :octocat:Code | 2022 |
| 20 | Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks | ⚔Attack | 📝ICDM | :octocat:Code | 2022 |
| 21 | Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection | ⚔Attack | 📝arXiv | :octocat:Code | 2022 |
| 22 | GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections | ⚔Attack | 📝arXiv | :octocat:Code | 2022 |
| 23 | Are Defenses for Graph Neural Networks Robust? | ⚔Attack | 📝NeurIPS | :octocat:Code | 2022 |
| 24 | Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias | ⚔Attack | 📝NeurIPS | :octocat:Code | 2022 |
| 25 | Structack: Structure-based Adversarial Attacks on Graph Neural Networks | ⚔Attack | 📝ACM Hypertext | :octocat:Code | 2021 |
| 26 | Graph Adversarial Attack via Rewiring | ⚔Attack | 📝KDD | :octocat:Code | 2021 |
| 27 | TDGIA: Effective Injection Attacks on Graph Neural Networks | ⚔Attack | 📝KDD | :octocat:Code | 2021 |
| 28 | Adversarial Attack on Large Scale Graph | ⚔Attack | 📝TKDE | :octocat:Code | 2021 |
| 29 | SAGE: Intrusion Alert-driven Attack Graph Extractor | ⚔Attack | 📝KDD Workshop | :octocat:Code | 2021 |
| 30 | Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models | ⚔Attack | 📝arXiv | :octocat:Code | 2021 |
| 31 | VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning | ⚔Attack | 📝PAKDD | :octocat:Code | 2021 |
| 32 | GraphAttacker: A General Multi-Task GraphAttack Framework | ⚔Attack | 📝arXiv | :octocat:Code | 2021 |
| 33 | Graph Stochastic Neural Networks for Semi-supervised Learning | ⚔Attack | 📝arXiv | :octocat:Code | 2021 |
| 34 | Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings | ⚔Attack | 📝arXiv | :octocat:Code | 2021 |
| 35 | Single-Node Attack for Fooling Graph Neural Networks | ⚔Attack | 📝KDD Workshop | :octocat:Code | 2021 |
| 36 | Poisoning Knowledge Graph Embeddings via Relation Inference Patterns | ⚔Attack | 📝ACL | :octocat:Code | 2021 |
| 37 | Single Node Injection Attack against Graph Neural Networks | ⚔Attack | 📝CIKM | :octocat:Code | 2021 |
| 38 | Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications | ⚔Attack | 📝ICDM | :octocat:Code | 2021 |
| 39 | Robustness of Graph Neural Networks at Scale | ⚔Attack | 📝NeurIPS | :octocat:Code | 2021 |
| 40 | Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models | ⚔Attack | 📝IJCAI | :octocat:Code | 2021 |
| 41 | Adversarial Attacks on Graph Classification via Bayesian Optimisation | ⚔Attack | 📝NeurIPS | :octocat:Code | 2021 |
| 42 | Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods | ⚔Attack | 📝EMNLP | :octocat:Code | 2021 |
| 43 | UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction | ⚔Attack | 📝ICCAD | :octocat:Code | 2021 |
| 44 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | ⚔Attack | 📝IJCAI | :octocat:Code | 2021 |
| 45 | Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation | ⚔Attack | 📝ICLR | :octocat:Code | 2020 |
| 46 | Towards More Practical Adversarial Attacks on Graph Neural Networks | ⚔Attack | 📝NeurIPS | :octocat:Code | 2020 |
| 47 | Adversarial Label-Flipping Attack and Defense for Graph Neural Networks | ⚔Attack | 📝ICDM | :octocat:Code | 2020 |
| 48 | Exploratory Adversarial Attacks on Graph Neural Networks | ⚔Attack | 📝ICDM | :octocat:Code | 2020 |
| 49 | A Targeted Universal Attack on Graph Convolutional Network | ⚔Attack | 📝arXiv | :octocat:Code | 2020 |
| 50 | Backdoor Attacks to Graph Neural Networks | ⚔Attack | 📝SACMAT | :octocat:Code | 2020 |
| 51 | Adversarial Attack on Community Detection by Hiding Individuals | ⚔Attack | 📝WWW | :octocat:Code | 2020 |
| 52 | A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models | ⚔Attack | 📝AAAI | :octocat:Code | 2020 |
| 53 | Scalable Attack on Graph Data by Injecting Vicious Nodes | ⚔Attack | 📝ECML-PKDD | :octocat:Code | 2020 |
| 54 | Network disruption: maximizing disagreement and polarization in social networks | ⚔Attack | 📝arXiv | :octocat:Code | 2020 |
| 55 | Structured Adversarial Attack Towards General Implementation and Better Interpretability | ⚔Attack | 📝ICLR | :octocat:Code | 2019 |
| 56 | PeerNets Exploiting Peer Wisdom Against Adversarial Attacks | ⚔Attack | 📝ICLR | :octocat:Code | 2019 |
| 57 | Adversarial Attacks on Node Embeddings via Graph Poisoning | ⚔Attack | 📝ICML | :octocat:Code | 2019 |
| 58 | Adversarial Attacks on Graph Neural Networks via Meta Learning | ⚔Attack | 📝ICLR | :octocat:Code | 2019 |
| 59 | Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective | ⚔Attack | 📝IJCAI | :octocat:Code | 2019 |
| 60 | Adversarial Examples on Graph Data: Deep Insights into Attack and Defense | ⚔Attack | 📝IJCAI | :octocat:Code | 2019 |
| 61 | A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning | ⚔Attack | 📝NeurIPS | :octocat:Code | 2019 |
| 62 | Adversarial Attacks on Neural Networks for Graph Data | ⚔Attack | 📝KDD | :octocat:Code | 2018 |
| 63 | Adversarial Attack on Graph Structured Data | ⚔Attack | 📝ICML | :octocat:Code | 2018 |
| 64 | Adversarial Sets for Regularising Neural Link Predictors | ⚔Attack | 📝UAI | :octocat:Code | 2017 |
| 65 | Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions | 🛡Defense | 📝NeurIPS | :octocat:Code | 2023 |
| 66 | Empowering Graph Representation Learning with Test-Time Graph Transformation | 🛡Defense | 📝ICLR | :octocat:Code | 2023 |
| 67 | Robust Training of Graph Neural Networks via Noise Governance | 🛡Defense | 📝WSDM | :octocat:Code | 2023 |
| 68 | Self-Supervised Graph Structure Refinement for Graph Neural Networks | 🛡Defense | 📝WSDM | :octocat:Code | 2023 |
| 69 | Revisiting Robustness in Graph Machine Learning | 🛡Defense | 📝ICLR | :octocat:Code | 2023 |
| 70 | Unsupervised Adversarially-Robust Representation Learning on Graphs | 🛡Defense | 📝AAAI | :octocat:Code | 2022 |
| 71 | Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels | 🛡Defense | 📝WSDM | :octocat:Code | 2022 |
| 72 | Mind Your Solver! On Adversarial Attack and Defense for Combinatorial Optimization | 🛡Defense | 📝arXiv | :octocat:Code | 2022 |
| 73 | Graph Neural Network for Local Corruption Recovery | 🛡Defense | 📝arXiv | :octocat:Code | 2022 |
| 74 | Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision | 🛡Defense | 📝AAAI | :octocat:Code | 2022 |
| 75 | SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation | 🛡Defense | 📝WWW | :octocat:Code | 2022 |
| 76 | GUARD: Graph Universal Adversarial Defense | 🛡Defense | 📝arXiv | :octocat:Code | 2022 |
| 77 | Bayesian Robust Graph Contrastive Learning | 🛡Defense | 📝arXiv | :octocat:Code | 2022 |
| 78 | Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN | 🛡Defense | 📝KDD | :octocat:Code | 2022 |
| 79 | Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond | 🛡Defense | 📝CVPR | :octocat:Code | 2022 |
| 80 | How does Heterophily Impact Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications | 🛡Defense | 📝KDD | :octocat:Code | 2022 |
| 81 | Robust Graph Neural Networks using Weighted Graph Laplacian | 🛡Defense | 📝SPCOM | :octocat:Code | 2022 |
| 82 | Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation | 🛡Defense | 📝KDD | :octocat:Code | 2022 |
| 83 | Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation | 🛡Defense | 📝CIKM | :octocat:Code | 2022 |
| 84 | On the Robustness of Graph Neural Diffusion to Topology Perturbations | 🛡Defense | 📝NeurIPS | :octocat:Code | 2022 |
| 85 | Spectral Adversarial Training for Robust Graph Neural Network | 🛡Defense | 📝TKDE | :octocat:Code | 2022 |
| 86 | You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets | 🛡Defense | 📝LoG | :octocat:Code | 2022 |
| 87 | Learning to Drop: Robust Graph Neural Network via Topological Denoising | 🛡Defense | 📝WSDM | :octocat:Code | 2021 |
| 88 | Understanding Structural Vulnerability in Graph Convolutional Networks | 🛡Defense | 📝IJCAI | :octocat:Code | 2021 |
| 89 | A Robust and Generalized Framework for Adversarial Graph Embedding | 🛡Defense | 📝arXiv | :octocat:Code | 2021 |
| 90 | Information Obfuscation of Graph Neural Network | 🛡Defense | 📝ICML | :octocat:Code | 2021 |
| 91 | Elastic Graph Neural Networks | 🛡Defense | 📝ICML | :octocat:Code | 2021 |
| 92 | Node Similarity Preserving Graph Convolutional Networks | 🛡Defense | 📝WSDM | :octocat:Code | 2021 |
| 93 | NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data | 🛡Defense | 📝TKDE | :octocat:Code | 2021 |
| 94 | Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering | 🛡Defense | 📝AAAI | :octocat:Code | 2021 |
| 95 | Unveiling the potential of Graph Neural Networks for robust Intrusion Detection | 🛡Defense | 📝arXiv | :octocat:Code | 2021 |
| 96 | A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks | 🛡Defense | 📝ICICS | :octocat:Code | 2021 |
| 97 | Node Feature Kernels Increase Graph Convolutional Network Robustness | 🛡Defense | 📝arXiv | :octocat:Code | 2021 |
| 98 | Not All Low-Pass Filters are Robust in Graph Convolutional Networks | 🛡Defense | 📝NeurIPS | :octocat:Code | 2021 |
| 99 | Graph Neural Networks with Adaptive Residual | 🛡Defense | 📝NeurIPS | :octocat:Code | 2021 |
| 100 | Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification | 🛡Defense | 📝NeurIPS | :octocat:Code | 2021 |
| 101 | Topological Relational Learning on Graphs | 🛡Defense | 📝NeurIPS | :octocat:Code | 2021 |
| 102 | Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings | 🛡Defense | 📝NeurIPS | :octocat:Code | 2020 |
| 103 | Graph Random Neural Networks for Semi-Supervised Learning on Graphs | 🛡Defense | 📝NeurIPS | :octocat:Code | 2020 |
| 104 | Reliable Graph Neural Networks via Robust Aggregation | 🛡Defense | 📝NeurIPS | :octocat:Code | 2020 |
| 105 | Graph Adversarial Networks: Protecting Information against Adversarial Attacks | 🛡Defense | 📝ICLR OpenReview | :octocat:Code | 2020 |
| 106 | A Feature-Importance-Aware and Robust Aggregator for GCN | 🛡Defense | 📝CIKM | :octocat:Code | 2020 |
| 107 | Graph Information Bottleneck | 🛡Defense | 📝NeurIPS | :octocat:Code | 2020 |
| 108 | Graph Contrastive Learning with Augmentations | 🛡Defense | 📝NeurIPS | :octocat:Code | 2020 |
| 109 | Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks | 🛡Defense | 📝None | :octocat:Code | 2020 |
| 110 | Adversarial Privacy Preserving Graph Embedding against Inference Attack | 🛡Defense | 📝arXiv | :octocat:Code | 2020 |
| 111 | GNNGuard: Defending Graph Neural Networks against Adversarial Attacks | 🛡Defense | 📝NeurIPS | :octocat:Code | 2020 |
| 112 | Transferring Robustness for Graph Neural Network Against Poisoning Attacks | 🛡Defense | 📝WSDM | :octocat:Code | 2020 |
| 113 | All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs | 🛡Defense | 📝WSDM | :octocat:Code | 2020 |
| 114 | Robust Detection of Adaptive Spammers by Nash Reinforcement Learning | 🛡Defense | 📝KDD | :octocat:Code | 2020 |
| 115 | Graph Structure Learning for Robust Graph Neural Networks | 🛡Defense | 📝KDD | :octocat:Code | 2020 |
| 116 | On The Stability of Polynomial Spectral Graph Filters | 🛡Defense | 📝ICASSP | :octocat:Code | 2020 |
| 117 | On the Robustness of Cascade Diffusion under Node Attacks | 🛡Defense | 📝WWW | :octocat:Code | 2020 |
| 118 | Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters | 🛡Defense | 📝CIKM | :octocat:Code | 2020 |
| 119 | DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder | 🛡Defense | 📝arXiv | :octocat:Code | 2020 |
| 120 | Graph-Revised Convolutional Network | 🛡Defense | 📝ECML-PKDD | :octocat:Code | 2020 |
| 121 | Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure | 🛡Defense | 📝TKDE | :octocat:Code | 2019 |
| 122 | Bayesian graph convolutional neural networks for semi-supervised classification | 🛡Defense | 📝AAAI | :octocat:Code | 2019 |
| 123 | Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning | 🛡Defense | 📝arXiv | :octocat:Code | 2019 |
| 124 | Adversarial Training Methods for Network Embedding | 🛡Defense | 📝WWW | :octocat:Code | 2019 |
| 125 | Batch Virtual Adversarial Training for Graph Convolutional Networks | 🛡Defense | 📝ICML | :octocat:Code | 2019 |
| 126 | Latent Adversarial Training of Graph Convolution Networks | 🛡Defense | 📝LRGSD@ICML | :octocat:Code | 2019 |
| 127 | Characterizing Malicious Edges targeting on Graph Neural Networks | 🛡Defense | 📝ICLR OpenReview | :octocat:Code | 2019 |
| 128 | Robust Graph Convolutional Networks Against Adversarial Attacks | 🛡Defense | 📝KDD | :octocat:Code | 2019 |
| 129 | Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications | 🛡Defense | 📝NAACL | :octocat:Code | 2019 |
| 130 | Adversarial Personalized Ranking for Recommendation | 🛡Defense | 📝SIGIR | :octocat:Code | 2018 |
| 131 | Hierarchical Randomized Smoothing | 🔐Certification | 📝NeurIPS'2023 | :octocat:Code | 2023 |
| 132 | (Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More | 🔐Certification | 📝NeurIPS'2023 | :octocat:Code | 2023 |
| 133 | Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks | 🔐Certification | 📝NeurIPS'2022 | :octocat:Code | 2022 |
| 134 | Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation | 🔐Certification | 📝KDD'2021 | :octocat:Code | 2021 |
| 135 | Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks | 🔐Certification | 📝ICLR'2021 | :octocat:Code | 2021 |
| 136 | Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks | 🔐Certification | 📝NeurIPS'2020 | :octocat:Code | 2020 |
| 137 | Efficient Robustness Certificates for Discrete Data: Sparsity - Aware Randomized Smoothing for Graphs, Images and More | 🔐Certification | 📝ICML'2020 | :octocat:Code | 2020 |
| 138 | Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation | 🔐Certification | 📝KDD'2020 | :octocat:Code | 2020 |
| 139 | Certifiable Robustness and Robust Training for Graph Convolutional Networks | 🔐Certification | 📝KDD'2019 | :octocat:Code | 2019 |
| 140 | Certifiable Robustness to Graph Perturbations | 🔐Certification | 📝NeurIPS'2019 | :octocat:Code | 2019 |
| 141 | Towards a Unified Framework for Fair and Stable Graph Representation Learning | ⚖Stability | 📝UAI'2021 | :octocat:Code | 2021 |
| 142 | Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data | ⚖Stability | 📝NeurIPS'2021 | :octocat:Code | 2021 |
| 143 | When Do GNNs Work: Understanding and Improving Neighborhood Aggregation | ⚖Stability | 📝IJCAI Workshop'2019 | :octocat:Code | 2019 |
| 144 | Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts | 🚀Others | 📝arXiv‘2023 | :octocat:Code | 2023 |
| 145 | A Systematic Evaluation of Node Embedding Robustness | 🚀Others | 📝LoG‘2022 | :octocat:Code | 2022 |
| 146 | FLAG: Adversarial Data Augmentation for Graph Neural Networks | 🚀Others | 📝arXiv'2020 | :octocat:Code | 2020 |
| 147 | Training Robust Graph Neural Network by Applying Lipschitz Constant Constraint | 🚀Others | 📝CentraleSupélec'2020 | :octocat:Code | 2020 |
| 148 | DeepRobust: a Platform for Adversarial Attacks and Defenses | ⚙Toolbox | 📝AAAI’2021 | :octocat:DeepRobust | 2021 |
| 149 | GreatX: A graph reliability toolbox based on PyTorch and PyTorch Geometric | ⚙Toolbox | 📝arXiv’2022 | :octocat:GreatX | 2022 |
| 150 | Evaluating Graph Vulnerability and Robustness using TIGER | ⚙Toolbox | 📝arXiv‘2021 | :octocat:TIGER | 2021 |
| 151 | Graph Robustness Benchmark: Rethinking and Benchmarking Adversarial Robustness of Graph Neural Networks | ⚙Toolbox | 📝NeurIPS'2021 | :octocat:Graph Robustness Benchmark (GRB) | 2021 |