| 0 |
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem |
⚔Attack |
📝WSDM |
 Code |
2022 |
| 1 |
Inference Attacks Against Graph Neural Networks |
⚔Attack |
📝USENIX Security |
Code |
2022 |
| 2 |
Model Stealing Attacks Against Inductive Graph Neural Networks |
⚔Attack |
📝IEEE Symposium on Security and Privacy |
Code |
2022 |
| 3 |
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation |
⚔Attack |
📝WWW |
Code |
2022 |
| 4 |
Neighboring Backdoor Attacks on Graph Convolutional Network |
⚔Attack |
📝arXiv |
Code |
2022 |
| 5 |
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability |
⚔Attack |
📝ICLR |
Code |
2022 |
| 6 |
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs |
⚔Attack |
📝AAAI |
Code |
2022 |
| 7 |
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2022 |
| 8 |
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection |
⚔Attack |
📝arXiv |
|
2022 |
| 9 |
Projective Ranking-based GNN Evasion Attacks |
⚔Attack |
📝arXiv |
|
2022 |
| 10 |
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation |
⚔Attack |
📝arXiv |
|
2022 |
| 11 |
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization |
⚔Attack |
📝Asia CCS |
Code |
2022 |
| 12 |
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees |
⚔Attack |
📝CVPR |
Code |
2022 |
| 13 |
Black-box Node Injection Attack for Graph Neural Networks |
⚔Attack |
📝arXiv |
Code |
2022 |
| 14 |
Stability and Generalization Capabilities of Message Passing Graph Neural Networks |
⚖Stability |
📝arXiv'2022 |
|
2022 |
| 15 |
On the Prediction Instability of Graph Neural Networks |
⚖Stability |
📝arXiv'2022 |
|
2022 |
| 16 |
GraphWar: A graph adversarial learning toolbox based on PyTorch and DGL |
⚙Toolbox |
📝arXiv’2022 |
GraphWar |
2022 |
| 17 |
A Comparative Study on Robust Graph Neural Networks to Structural Noises |
📃Survey |
📝AAAI DLG'2022 |
|
2022 |
| 18 |
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack |
📃Survey |
📝arXiv'2022 |
|
2022 |
| 19 |
Graph Vulnerability and Robustness: A Survey |
📃Survey |
📝TKDE'2022 |
|
2022 |
| 20 |
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability |
📃Survey |
arXiv'2022 |
|
2022 |
| 21 |
Trustworthy Graph Neural Networks: Aspects, Methods and Trends |
📃Survey |
arXiv'2022 |
|
2022 |
| 22 |
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection |
📃Survey |
arXiv'2022 |
|
2022 |
| 23 |
Unsupervised Adversarially-Robust Representation Learning on Graphs |
🛡Defense |
📝AAAI |
Code |
2022 |
| 24 |
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels |
🛡Defense |
📝arXiv |
Code |
2022 |
| 25 |
Mind Your Solver! On Adversarial Attack and Defense for Combinatorial Optimization |
🛡Defense |
📝arXiv |
Code |
2022 |
| 26 |
Learning Robust Representation through Graph Adversarial Contrastive Learning |
🛡Defense |
📝arXiv |
|
2022 |
| 27 |
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2022 |
| 28 |
Graph Neural Network for Local Corruption Recovery |
🛡Defense |
📝arXiv |
Code |
2022 |
| 29 |
Robust Heterogeneous Graph Neural Networks against Adversarial Attacks |
🛡Defense |
📝AAAI |
|
2022 |
| 30 |
How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks? |
🛡Defense |
📝Neural Processing Letters |
|
2022 |
| 31 |
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision |
🛡Defense |
📝AAAI |
Code |
2022 |
| 32 |
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation |
🛡Defense |
📝WWW |
Code |
2022 |
| 33 |
Exploring High-Order Structure for Robust Graph Structure Learning |
🛡Defense |
📝arXiv |
|
2022 |
| 34 |
Detecting Topology Attacks against Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2022 |
| 35 |
LPGNet: Link Private Graph Networks for Node Classification |
🛡Defense |
📝arXiv |
|
2022 |
| 36 |
GUARD: Graph Universal Adversarial Defense |
🛡Defense |
📝arXiv |
Code |
2022 |
| 37 |
How Members of Covert Networks Conceal the Identities of Their Leaders |
⚔Attack |
📝ACM TIST |
|
2021 |
| 38 |
Task and Model Agnostic Adversarial Attack on Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 39 |
FHA: Fast Heuristic Attack Against Graph Convolutional Networks |
⚔Attack |
📝ICDS |
|
2021 |
| 40 |
Adversarial Attack against Cross-lingual Knowledge Graph Alignment |
⚔Attack |
📝EMNLP |
|
2021 |
| 41 |
Structural Attack against Graph Based Android Malware Detection |
⚔Attack |
📝CCS |
|
2021 |
| 42 |
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction |
⚔Attack |
📝ICCAD |
Code |
2021 |
| 43 |
VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning |
⚔Attack |
📝PAKDD |
Code |
2021 |
| 44 |
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models |
⚔Attack |
📝arXiv |
Code |
2021 |
| 45 |
SAGE: Intrusion Alert-driven Attack Graph Extractor |
⚔Attack |
📝KDD Workshop |
Code |
2021 |
| 46 |
Universal Spectral Adversarial Attacks for Deformable Shapes |
⚔Attack |
📝CVPR |
|
2021 |
| 47 |
Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 48 |
GraphMI: Extracting Private Graph Data from Graph Neural Networks |
⚔Attack |
📝IJCAI |
Code |
2021 |
| 49 |
Adversarial Attack on Large Scale Graph |
⚔Attack |
📝TKDE |
Code |
2021 |
| 50 |
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge |
⚔Attack |
📝arXiv |
|
2021 |
| 51 |
TDGIA: Effective Injection Attacks on Graph Neural Networks |
⚔Attack |
📝KDD |
Code |
2021 |
| 52 |
Graph Backdoor |
⚔Attack |
📝USENIX Security |
|
2021 |
| 53 |
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection |
⚔Attack |
📝arXiv |
|
2021 |
| 54 |
Membership Inference Attack on Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 55 |
Graph Adversarial Attack via Rewiring |
⚔Attack |
📝KDD |
Code |
2021 |
| 56 |
GReady for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack |
⚔Attack |
📝Information Sciences |
|
2021 |
| 57 |
Optimal Edge Weight Perturbations to Attack Shortest Paths |
⚔Attack |
📝arXiv |
|
2021 |
| 58 |
Structack: Structure-based Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝ACM Hypertext |
Code |
2021 |
| 59 |
PATHATTACK: Attacking Shortest Paths in Complex Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 60 |
Explainability-based Backdoor Attacks Against Graph Neural Networks |
⚔Attack |
📝WiseML@WiSec |
|
2021 |
| 61 |
Stealing Links from Graph Neural Networks |
⚔Attack |
📝USENIX Security |
|
2021 |
| 62 |
GraphAttacker: A General Multi-Task GraphAttack Framework |
⚔Attack |
📝arXiv |
Code |
2021 |
| 63 |
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense |
⚔Attack |
📝arXiv |
|
2021 |
| 64 |
Node-Level Membership Inference Attacks Against Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 65 |
COREATTACK: Breaking Up the Core Structure of Graphs |
⚔Attack |
📝arXiv |
|
2021 |
| 66 |
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods |
⚔Attack |
📝EMNLP |
Code |
2021 |
| 67 |
Adversarial Attacks on Graph Classification via Bayesian Optimisation |
⚔Attack |
📝NeurIPS |
Code |
2021 |
| 68 |
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models |
⚔Attack |
📝IJCAI |
Code |
2021 |
| 69 |
Graph Structural Attack by Spectral Distance |
⚔Attack |
📝arXiv |
|
2021 |
| 70 |
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness |
⚔Attack |
📝NeurIPS |
|
2021 |
| 71 |
Robustness of Graph Neural Networks at Scale |
⚔Attack |
📝NeurIPS |
Code |
2021 |
| 72 |
Attacking Graph Neural Networks at Scale |
⚔Attack |
📝AAAI workshop |
|
2021 |
| 73 |
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks |
⚔Attack |
📝arXiv |
|
2021 |
| 74 |
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications |
⚔Attack |
📝ICDM |
Code |
2021 |
| 75 |
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning |
⚔Attack |
📝arXiv |
|
2021 |
| 76 |
Time-aware Gradient Attack on Dynamic Network Link Prediction |
⚔Attack |
📝TKDE |
|
2021 |
| 77 |
Query-based Adversarial Attacks on Graph with Fake Nodes |
⚔Attack |
📝arXiv |
|
2021 |
| 78 |
Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks |
⚔Attack |
📝CIKM |
|
2021 |
| 79 |
Derivative-free optimization adversarial attacks for graph convolutional networks |
⚔Attack |
📝PeerJ |
|
2021 |
| 80 |
Watermarking Graph Neural Networks based on Backdoor Attacks |
⚔Attack |
📝arXiv |
|
2021 |
| 81 |
Single Node Injection Attack against Graph Neural Networks |
⚔Attack |
📝CIKM |
Code |
2021 |
| 82 |
Spatially Focused Attack against Spatiotemporal Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 83 |
Reinforcement Learning For Data Poisoning on Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
| 84 |
Graphfool: Targeted Label Adversarial Attack on Graph Embedding |
⚔Attack |
📝arXiv |
|
2021 |
| 85 |
Towards Revealing Parallel Adversarial Attack on Politician Socialnet of Graph Structure |
⚔Attack |
📝Security and Communication Networks |
|
2021 |
| 86 |
Network Embedding Attack: An Euclidean Distance Based Method |
⚔Attack |
📝MDATA |
|
2021 |
| 87 |
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation |
⚔Attack |
📝arXiv |
|
2021 |
| 88 |
Jointly Attacking Graph Neural Network and its Explanations |
⚔Attack |
📝arXiv |
|
2021 |
| 89 |
DeHiB: Deep Hidden Backdoor Attack on Semi-Supervised Learning via Adversarial Perturbation |
⚔Attack |
📝AAAI |
|
2021 |
| 90 |
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings |
⚔Attack |
📝arXiv |
Code |
2021 |
| 91 |
Single-Node Attack for Fooling Graph Neural Networks |
⚔Attack |
📝KDD Workshop |
Code |
2021 |
| 92 |
The Robustness of Graph k-shell Structure under Adversarial Attacks |
⚔Attack |
📝arXiv |
|
2021 |
| 93 |
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns |
⚔Attack |
📝ACL |
Code |
2021 |
| 94 |
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks |
⚔Attack |
📝CCS |
|
2021 |
| 95 |
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking |
⚔Attack |
📝DATE Conference |
|
2021 |
| 96 |
Graph Stochastic Neural Networks for Semi-supervised Learning |
⚔Attack |
📝arXiv |
Code |
2021 |
| 97 |
Towards a Unified Framework for Fair and Stable Graph Representation Learning |
⚖Stability |
📝UAI'2021 |
Code |
2021 |
| 98 |
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data |
⚖Stability |
📝arXiv'2021 |
|
2021 |
| 99 |
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations |
⚖Stability |
📝arXiv'2021 |
|
2021 |
| 100 |
Training Stable Graph Neural Networks Through Constrained Learning |
⚖Stability |
📝arXiv'2021 |
|
2021 |
| 101 |
DeepRobust: a Platform for Adversarial Attacks and Defenses |
⚙Toolbox |
📝AAAI’2021 |
DeepRobust |
2021 |
| 102 |
Evaluating Graph Vulnerability and Robustness using TIGER |
⚙Toolbox |
📝arXiv‘2021 |
TIGER |
2021 |
| 103 |
Graph Robustness Benchmark: Rethinking and Benchmarking Adversarial Robustness of Graph Neural Networks |
⚙Toolbox |
📝NeurIPS Openreview’2021 |
Graph Robustness Benchmark (GRB) |
2021 |
| 104 |
Deep Graph Structure Learning for Robust Representations: A Survey |
📃Survey |
📝arXiv'2021 |
|
2021 |
| 105 |
Robustness of deep learning models on graphs: A survey |
📃Survey |
📝AI Open'2021 |
|
2021 |
| 106 |
Graph Neural Networks Methods, Applications, and Opportunities |
📃Survey |
📝arXiv'2021 |
|
2021 |
| 107 |
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies |
📃Survey |
📝SIGKDD Explorations'2021 |
|
2021 |
| 108 |
Robust Certification for Laplace Learning on Geometric Graphs |
🔐Certification |
📝MSML’2021 |
|
2021 |
| 109 |
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning |
🔐Certification |
📝ICLR OpenReview'2021 |
|
2021 |
| 110 |
Adversarial Immunization for Improving Certifiable Robustness on Graphs |
🔐Certification |
📝WSDM'2021 |
|
2021 |
| 111 |
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks |
🔐Certification |
📝ICLR'2021 |
Code |
2021 |
| 112 |
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation |
🔐Certification |
📝KDD'2021 |
Code |
2021 |
| 113 |
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks |
🚀Others |
📝arXiv'2021 |
|
2021 |
| 114 |
SIGL: Securing Software Installations Through Deep Graph Learning |
🚀Others |
📝USENIX'2021 |
|
2021 |
| 115 |
A Robust and Generalized Framework for Adversarial Graph Embedding |
🛡Defense |
📝arXiv |
Code |
2021 |
| 116 |
Learning to Drop: Robust Graph Neural Network via Topological Denoising |
🛡Defense |
📝WSDM |
Code |
2021 |
| 117 |
How effective are Graph Neural Networks in Fraud Detection for Network Data? |
🛡Defense |
📝arXiv |
|
2021 |
| 118 |
An Introduction to Robust Graph Convolutional Networks |
🛡Defense |
📝arXiv |
|
2021 |
| 119 |
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System |
🛡Defense |
📝arXiv |
|
2021 |
| 120 |
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks |
🛡Defense |
📝arXiv |
|
2021 |
| 121 |
Robust graph convolutional networks with directional graph adversarial training |
🛡Defense |
📝Applied Intelligence |
|
2021 |
| 122 |
Detection and Defense of Topological Adversarial Attacks on Graphs |
🛡Defense |
📝AISTATS |
|
2021 |
| 123 |
Unveiling the potential of Graph Neural Networks for robust Intrusion Detection |
🛡Defense |
📝arXiv |
Code |
2021 |
| 124 |
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction |
🛡Defense |
📝arXiv |
|
2021 |
| 125 |
EGC2: Enhanced Graph Classification with Easy Graph Compression |
🛡Defense |
📝arXiv |
|
2021 |
| 126 |
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis |
🛡Defense |
📝arXiv |
|
2021 |
| 127 |
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck |
🛡Defense |
📝IJCNN |
|
2021 |
| 128 |
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights |
🛡Defense |
📝arXiv |
|
2021 |
| 129 |
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph |
🛡Defense |
📝arXiv |
|
2021 |
| 130 |
Releasing Graph Neural Networks with Differential Privacy Guarantees |
🛡Defense |
📝arXiv |
|
2021 |
| 131 |
Speedup Robust Graph Structure Learning with Low-Rank Information |
🛡Defense |
📝CIKM |
|
2021 |
| 132 |
A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks |
🛡Defense |
📝ICICS |
Code |
2021 |
| 133 |
Node Feature Kernels Increase Graph Convolutional Network Robustness |
🛡Defense |
📝arXiv |
Code |
2021 |
| 134 |
On the Relationship between Heterophily and Robustness of Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2021 |
| 135 |
Unified Robust Training for Graph NeuralNetworks against Label Noise |
🛡Defense |
📝arXiv |
|
2021 |
| 136 |
Randomized Generation of Adversary-Aware Fake Knowledge Graphs to Combat Intellectual Property Theft |
🛡Defense |
📝AAAI |
|
2021 |
| 137 |
Interpretable Stability Bounds for Spectral Graph Filters |
🛡Defense |
📝arXiv |
|
2021 |
| 138 |
Personalized privacy protection in social networks through adversarial modeling |
🛡Defense |
📝AAAI |
|
2021 |
| 139 |
Integrated Defense for Resilient Graph Matching |
🛡Defense |
📝ICML |
|
2021 |
| 140 |
Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs |
🛡Defense |
📝ICASSP |
|
2021 |
| 141 |
Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination |
🛡Defense |
📝WWW |
|
2021 |
| 142 |
Information Obfuscation of Graph Neural Network |
🛡Defense |
📝ICML |
Code |
2021 |
| 143 |
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs |
🛡Defense |
📝arXiv |
|
2021 |
| 144 |
On Generalization of Graph Autoencoders with Adversarial Training |
🛡Defense |
📝ECML |
|
2021 |
| 145 |
DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs |
🛡Defense |
📝ECML |
|
2021 |
| 146 |
Graph Sanitation with Application to Node Classification |
🛡Defense |
📝arXiv |
|
2021 |
| 147 |
Distributionally Robust Semi-Supervised Learning Over Graphs |
🛡Defense |
📝ICLR |
|
2021 |
| 148 |
Robust Counterfactual Explanations on Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2021 |
| 149 |
Enhancing Robustness and Resilience of Multiplex Networks Against Node-Community Cascading Failures |
🛡Defense |
📝IEEE TSMC |
|
2021 |
| 150 |
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data |
🛡Defense |
📝TKDE |
Code |
2021 |
| 151 |
Robust Graph Learning Under Wasserstein Uncertainty |
🛡Defense |
📝arXiv |
|
2021 |
| 152 |
Towards Robust Graph Contrastive Learning |
🛡Defense |
📝arXiv |
|
2021 |
| 153 |
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks |
🛡Defense |
📝ICML |
|
2021 |
| 154 |
UAG: Uncertainty-Aware Attention Graph Neural Network for Defending Adversarial Attacks |
🛡Defense |
📝AAAI |
|
2021 |
| 155 |
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks |
🛡Defense |
📝AAAI |
|
2021 |
| 156 |
Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering |
🛡Defense |
📝AAAI |
Code |
2021 |
| 157 |
Node Similarity Preserving Graph Convolutional Networks |
🛡Defense |
📝WSDM |
Code |
2021 |
| 158 |
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation |
🛡Defense |
📝arXiv |
|
2021 |
| 159 |
Not All Low-Pass Filters are Robust in Graph Convolutional Networks |
🛡Defense |
📝NeurIPS |
Code |
2021 |
| 160 |
Towards Robust Reasoning over Knowledge Graphs |
🛡Defense |
📝arXiv |
|
2021 |
| 161 |
Understanding Structural Vulnerability in Graph Convolutional Networks |
🛡Defense |
📝IJCAI |
Code |
2021 |
| 162 |
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints |
🛡Defense |
📝arXiv |
|
2021 |
| 163 |
Graph Neural Networks with Adaptive Residual |
🛡Defense |
NeurIPS |
Code |
2021 |
| 164 |
Graph-based Adversarial Online Kernel Learning with Adaptive Embedding |
🛡Defense |
📝ICDM |
|
2021 |
| 165 |
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification |
🛡Defense |
📝NeurIPS |
Code |
2021 |
| 166 |
Graph Neural Networks with Feature and Structure Aware Random Walk |
🛡Defense |
📝arXiv |
|
2021 |
| 167 |
Topological Relational Learning on Graphs |
🛡Defense |
📝NeurIPS |
Code |
2021 |
| 168 |
Elastic Graph Neural Networks |
🛡Defense |
📝ICML |
Code |
2021 |
| 169 |
Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns |
⚔Attack |
📝TKDD |
|
2020 |
| 170 |
Practical Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝ICML Workshop |
|
2020 |
| 171 |
An Efficient Adversarial Attack on Graph Structured Data |
⚔Attack |
📝IJCAI Workshop |
|
2020 |
| 172 |
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach |
⚔Attack |
📝WWW |
|
2020 |
| 173 |
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks |
⚔Attack |
📝BigData |
|
2020 |
| 174 |
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models |
⚔Attack |
📝AAAI |
Code |
2020 |
| 175 |
Manipulating Node Similarity Measures in Networks |
⚔Attack |
📝AAMAS |
|
2020 |
| 176 |
Adversarial Attack on Community Detection by Hiding Individuals |
⚔Attack |
📝WWW |
Code |
2020 |
| 177 |
Adversarial Attack on Hierarchical Graph Pooling Neural Networks |
⚔Attack |
📝arXiv |
|
2020 |
| 178 |
Link Prediction Adversarial Attack Via Iterative Gradient Attack |
⚔Attack |
📝IEEE Trans |
|
2020 |
| 179 |
Backdoor Attacks to Graph Neural Networks |
⚔Attack |
📝SACMAT |
Code |
2020 |
| 180 |
Efficient Evasion Attacks to Graph Neural Networks via Influence Function |
⚔Attack |
📝arXiv |
|
2020 |
| 181 |
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs |
⚔Attack |
📝arXiv |
|
2020 |
| 182 |
Query-free Black-box Adversarial Attacks on Graphs |
⚔Attack |
📝arXiv |
|
2020 |
| 183 |
Adversarial Attacks on Link Prediction Algorithms Based on Graph Neural Networks |
⚔Attack |
📝Asia CCS |
|
2020 |
| 184 |
A Targeted Universal Attack on Graph Convolutional Network |
⚔Attack |
📝arXiv |
Code |
2020 |
| 185 |
Adversarial Label-Flipping Attack and Defense for Graph Neural Networks |
⚔Attack |
📝ICDM |
Code |
2020 |
| 186 |
Towards More Practical Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝NeurIPS |
Code |
2020 |
| 187 |
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation |
⚔Attack |
📝ICLR |
Code |
2020 |
| 188 |
Cross Entropy Attack on Deep Graph Infomax |
⚔Attack |
📝IEEE ISCAS |
|
2020 |
| 189 |
Attacking Graph-Based Classification without Changing Existing Connections |
⚔Attack |
📝ACSAC |
|
2020 |
| 190 |
Adversarial Attacks on Deep Graph Matching |
⚔Attack |
📝NeurIPS |
|
2020 |
| 191 |
Near-Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem |
⚔Attack |
📝ICLR OpenReview |
|
2020 |
| 192 |
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting |
⚔Attack |
📝ICLR OpenReview |
|
2020 |
| 193 |
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers |
⚔Attack |
📝arXiv |
|
2020 |
| 194 |
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection |
⚔Attack |
📝arXiv |
|
2020 |
| 195 |
A Graph Matching Attack on Privacy-Preserving Record Linkage |
⚔Attack |
📝CIKM |
|
2020 |
| 196 |
Exploratory Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝ICDM |
Code |
2020 |
| 197 |
Scalable Attack on Graph Data by Injecting Vicious Nodes |
⚔Attack |
📝ECML-PKDD |
Code |
2020 |
| 198 |
Attackability Characterization of Adversarial Evasion Attack on Discrete Data |
⚔Attack |
📝KDD |
|
2020 |
| 199 |
MGA: Momentum Gradient Attack on Network |
⚔Attack |
📝arXiv |
|
2020 |
| 200 |
Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria |
⚔Attack |
📝arXiv |
|
2020 |
| 201 |
Adversarial Perturbations of Opinion Dynamics in Networks |
⚔Attack |
📝arXiv |
|
2020 |
| 202 |
Network disruption: maximizing disagreement and polarization in social networks |
⚔Attack |
📝arXiv |
Code |
2020 |
| 203 |
Adversarial attack on BC classification for scale-free networks |
⚔Attack |
📝AIP Chaos |
|
2020 |
| 204 |
Adaptive Adversarial Attack on Graph Embedding via GAN |
⚔Attack |
📝SocialSec |
|
2020 |
| 205 |
On the Stability of Graph Convolutional Neural Networks under Edge Rewiring |
⚖Stability |
📝arXiv'2020 |
|
2020 |
| 206 |
Stability of Graph Neural Networks to Relative Perturbations |
⚖Stability |
📝ICASSP'2020 |
|
2020 |
| 207 |
Graph Neural Networks: Architectures, Stability and Transferability |
⚖Stability |
📝arXiv'2020 |
|
2020 |
| 208 |
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method |
⚖Stability |
📝arXiv'2020 |
|
2020 |
| 209 |
Graph and Graphon Neural Network Stability |
⚖Stability |
📝arXiv'2020 |
|
2020 |
| 210 |
A Survey of Adversarial Learning on Graph |
📃Survey |
📝arXiv'2020 |
|
2020 |
| 211 |
Graph Neural Networks Taxonomy, Advances and Trends |
📃Survey |
📝arXiv'2020 |
|
2020 |
| 212 |
Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning |
🔐Certification |
📝AAAI'2020 |
|
2020 |
| 213 |
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks |
🔐Certification |
📝NeurIPS'2020 |
Code |
2020 |
| 214 |
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation |
🔐Certification |
📝KDD'2020 |
Code |
2020 |
| 215 |
Efficient Robustness Certificates for Discrete Data: Sparsity - Aware Randomized Smoothing for Graphs, Images and More |
🔐Certification |
📝ICML'2020 |
Code |
2020 |
| 216 |
Abstract Interpretation based Robustness Certification for Graph Convolutional Networks |
🔐Certification |
📝ECAI'2020 |
|
2020 |
| 217 |
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing |
🔐Certification |
📝GLOBECOM'2020 |
|
2020 |
| 218 |
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing |
🔐Certification |
📝WWW'2020 |
|
2020 |
| 219 |
FLAG: Adversarial Data Augmentation for Graph Neural Networks |
🚀Others |
📝arXiv'2020 |
Code |
2020 |
| 220 |
Watermarking Graph Neural Networks by Random Graphs |
🚀Others |
📝arXiv'2020 |
|
2020 |
| 221 |
Training Robust Graph Neural Network by Applying Lipschitz Constant Constraint |
🚀Others |
📝CentraleSupélec'2020 |
Code |
2020 |
| 222 |
When Does Self-Supervision Help Graph Convolutional Networks? |
🚀Others |
📝ICML'2020 |
|
2020 |
| 223 |
Graph-Revised Convolutional Network |
🛡Defense |
📝ECML-PKDD |
Code |
2020 |
| 224 |
Topological Effects on Attacks Against Vertex Classification |
🛡Defense |
📝arXiv |
|
2020 |
| 225 |
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning |
🛡Defense |
📝arXiv |
|
2020 |
| 226 |
DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder |
🛡Defense |
📝arXiv |
Code |
2020 |
| 227 |
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning |
🛡Defense |
📝arXiv |
|
2020 |
| 228 |
AANE: Anomaly Aware Network Embedding For Anomalous Link Detection |
🛡Defense |
📝ICDM |
|
2020 |
| 229 |
Provably Robust Node Classification via Low-Pass Message Passing |
🛡Defense |
📝ICDM |
|
2020 |
| 230 |
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters |
🛡Defense |
📝CIKM |
Code |
2020 |
| 231 |
Robust Collective Classification against Structural Attacks |
🛡Defense |
📝Preprint |
|
2020 |
| 232 |
Robust Training of Graph Convolutional Networks via Latent Perturbation |
🛡Defense |
📝ECML-PKDD |
|
2020 |
| 233 |
Robust Graph Representation Learning via Neural Sparsification |
🛡Defense |
📝ICML |
|
2020 |
| 234 |
I-GCN: Robust Graph Convolutional Network via Influence Mechanism |
🛡Defense |
📝arXiv |
|
2020 |
| 235 |
Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks |
🛡Defense |
📝AAAI |
|
2020 |
| 236 |
Smoothing Adversarial Training for GNN |
🛡Defense |
📝IEEE TCSS |
|
2020 |
| 237 |
Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks |
🛡Defense |
📝None |
Code |
2020 |
| 238 |
RoGAT: a robust GNN combined revised GAT with adjusted graphs |
🛡Defense |
📝arXiv |
|
2020 |
| 239 |
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks |
🛡Defense |
📝arXiv |
|
2020 |
| 240 |
Adversarial Privacy Preserving Graph Embedding against Inference Attack |
🛡Defense |
📝arXiv |
Code |
2020 |
| 241 |
Robust Graph Learning From Noisy Data |
🛡Defense |
📝IEEE Trans |
|
2020 |
| 242 |
Learning Graph Embedding with Adversarial Training Methods |
🛡Defense |
📝IEEE Transactions on Cybernetics |
|
2020 |
| 243 |
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks |
🛡Defense |
📝NeurIPS |
Code |
2020 |
| 244 |
All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs |
🛡Defense |
📝WSDM |
Code |
2020 |
| 245 |
How Robust Are Graph Neural Networks to Structural Noise? |
🛡Defense |
📝DLGMA |
|
2020 |
| 246 |
Robust Detection of Adaptive Spammers by Nash Reinforcement Learning |
🛡Defense |
📝KDD |
Code |
2020 |
| 247 |
Graph Structure Learning for Robust Graph Neural Networks |
🛡Defense |
📝KDD |
Code |
2020 |
| 248 |
On The Stability of Polynomial Spectral Graph Filters |
🛡Defense |
📝ICASSP |
Code |
2020 |
| 249 |
On the Robustness of Cascade Diffusion under Node Attacks |
🛡Defense |
📝WWW |
Code |
2020 |
| 250 |
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks |
🛡Defense |
📝WWW |
|
2020 |
| 251 |
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks |
🛡Defense |
📝ICASSP |
|
2020 |
| 252 |
Transferring Robustness for Graph Neural Network Against Poisoning Attacks |
🛡Defense |
📝WSDM |
Code |
2020 |
| 253 |
Graph Contrastive Learning with Augmentations |
🛡Defense |
📝NeurIPS |
Code |
2020 |
| 254 |
Graph Information Bottleneck |
🛡Defense |
📝NeurIPS |
Code |
2020 |
| 255 |
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach |
🛡Defense |
📝ICLR OpenReview |
|
2020 |
| 256 |
Provable Overlapping Community Detection in Weighted Graphs |
🛡Defense |
📝NeurIPS |
|
2020 |
| 257 |
Adversarial Detection on Graph Structured Data |
🛡Defense |
📝PPMLP |
|
2020 |
| 258 |
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings |
🛡Defense |
📝NeurIPS |
Code |
2020 |
| 259 |
Reliable Graph Neural Networks via Robust Aggregation |
🛡Defense |
📝NeurIPS |
Code |
2020 |
| 260 |
Towards Robust Graph Neural Networks against Label Noise |
🛡Defense |
📝ICLR OpenReview |
|
2020 |
| 261 |
Graph Adversarial Networks: Protecting Information against Adversarial Attacks |
🛡Defense |
📝ICLR OpenReview |
Code |
2020 |
| 262 |
A Novel Defending Scheme for Graph-Based Classification Against Graph Structure Manipulating Attack |
🛡Defense |
📝SocialSec |
|
2020 |
| 263 |
Node Copying for Protection Against Graph Neural Network Topology Attacks |
🛡Defense |
📝arXiv |
|
2020 |
| 264 |
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian |
🛡Defense |
📝NeurIPS |
|
2020 |
| 265 |
A Feature-Importance-Aware and Robust Aggregator for GCN |
🛡Defense |
📝CIKM |
Code |
2020 |
| 266 |
Anti-perturbation of Online Social Networks by Graph Label Transition |
🛡Defense |
📝arXiv |
|
2020 |
| 267 |
Graph Random Neural Networks for Semi-Supervised Learning on Graphs |
🛡Defense |
📝NeurIPS |
Code |
2020 |
| 268 |
Attacking Graph-based Classification via Manipulating the Graph Structure |
⚔Attack |
📝CCS |
|
2019 |
| 269 |
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning |
⚔Attack |
📝NeurIPS |
Code |
2019 |
| 270 |
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense |
⚔Attack |
📝IJCAI |
Code |
2019 |
| 271 |
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective |
⚔Attack |
📝IJCAI |
Code |
2019 |
| 272 |
Adversarial Attacks on Graph Neural Networks via Meta Learning |
⚔Attack |
📝ICLR |
Code |
2019 |
| 273 |
Data Poisoning Attack against Knowledge Graph Embedding |
⚔Attack |
📝IJCAI |
|
2019 |
| 274 |
GA Based Q-Attack on Community Detection |
⚔Attack |
📝TCSS |
|
2019 |
| 275 |
Attacking Graph Convolutional Networks via Rewiring |
⚔Attack |
📝arXiv |
|
2019 |
| 276 |
Unsupervised Euclidean Distance Attack on Network Embedding |
⚔Attack |
📝arXiv |
|
2019 |
| 277 |
Structured Adversarial Attack Towards General Implementation and Better Interpretability |
⚔Attack |
📝ICLR |
Code |
2019 |
| 278 |
Vertex Nomination, Consistent Estimation, and Adversarial Modification |
⚔Attack |
📝arXiv |
|
2019 |
| 279 |
PeerNets Exploiting Peer Wisdom Against Adversarial Attacks |
⚔Attack |
📝ICLR |
Code |
2019 |
| 280 |
Network Structural Vulnerability A Multi-Objective Attacker Perspective |
⚔Attack |
📝IEEE Trans |
|
2019 |
| 281 |
Multiscale Evolutionary Perturbation Attack on Community Detection |
⚔Attack |
📝arXiv |
|
2019 |
| 282 |
αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model |
⚔Attack |
📝CIKM |
|
2019 |
| 283 |
Adversarial Attacks on Node Embeddings via Graph Poisoning |
⚔Attack |
📝ICML |
Code |
2019 |
| 284 |
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling |
⚔Attack |
📝arXiv |
|
2019 |
| 285 |
When Do GNNs Work: Understanding and Improving Neighborhood Aggregation |
⚖Stability |
📝IJCAI Workshop'2019 |
Code |
2019 |
| 286 |
Stability Properties of Graph Neural Networks |
⚖Stability |
📝arXiv'2019 |
|
2019 |
| 287 |
Stability and Generalization of Graph Convolutional Neural Networks |
⚖Stability |
📝KDD'2019 |
|
2019 |
| 288 |
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review |
📃Survey |
📝arXiv'2019 |
|
2019 |
| 289 |
Certifiable Robustness and Robust Training for Graph Convolutional Networks |
🔐Certification |
📝KDD'2019 |
Code |
2019 |
| 290 |
Certifiable Robustness to Graph Perturbations |
🔐Certification |
📝NeurIPS'2019 |
Code |
2019 |
| 291 |
Perturbation Sensitivity of GNNs |
🚀Others |
📝cs224w'2019 |
|
2019 |
| 292 |
Adversarial Training Methods for Network Embedding |
🛡Defense |
📝WWW |
Code |
2019 |
| 293 |
Improving Robustness to Attacks Against Vertex Classification |
🛡Defense |
📝MLG@KDD |
|
2019 |
| 294 |
Can Adversarial Network Attack be Defended? |
🛡Defense |
📝arXiv |
|
2019 |
| 295 |
Adversarial Robustness of Similarity-Based Link Prediction |
🛡Defense |
📝ICDM |
|
2019 |
| 296 |
GraphDefense: Towards Robust Graph Convolutional Networks |
🛡Defense |
📝arXiv |
|
2019 |
| 297 |
GraphSAC: Detecting anomalies in large-scale graphs |
🛡Defense |
📝arXiv |
|
2019 |
| 298 |
Edge Dithering for Robust Adaptive Graph Convolutional Networks |
🛡Defense |
📝arXiv |
|
2019 |
| 299 |
Batch Virtual Adversarial Training for Graph Convolutional Networks |
🛡Defense |
📝ICML |
Code |
2019 |
| 300 |
Bayesian graph convolutional neural networks for semi-supervised classification |
🛡Defense |
📝AAAI |
Code |
2019 |
| 301 |
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations |
🛡Defense |
📝arXiv |
|
2019 |
| 302 |
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems |
🛡Defense |
📝arXiv |
|
2019 |
| 303 |
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique |
🛡Defense |
📝arXiv |
|
2019 |
| 304 |
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning |
🛡Defense |
📝arXiv |
Code |
2019 |
| 305 |
Adversarial Defense Framework for Graph Neural Network |
🛡Defense |
📝arXiv |
|
2019 |
| 306 |
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure |
🛡Defense |
📝TKDE |
Code |
2019 |
| 307 |
Latent Adversarial Training of Graph Convolution Networks |
🛡Defense |
📝LRGSD@ICML |
Code |
2019 |
| 308 |
Comparing and Detecting Adversarial Attacks for Graph Deep Learning |
🛡Defense |
📝RLGM@ICLR |
|
2019 |
| 309 |
Characterizing Malicious Edges targeting on Graph Neural Networks |
🛡Defense |
📝ICLR OpenReview |
Code |
2019 |
| 310 |
Robust Graph Data Learning via Latent Graph Convolutional Representation |
🛡Defense |
📝arXiv |
|
2019 |
| 311 |
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications |
🛡Defense |
📝NAACL |
Code |
2019 |
| 312 |
Robust Graph Convolutional Networks Against Adversarial Attacks |
🛡Defense |
📝KDD |
Code |
2019 |
| 313 |
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification |
🛡Defense |
📝PRCV |
|
2019 |
| 314 |
Adversarial Attack on Graph Structured Data |
⚔Attack |
📝ICML |
Code |
2018 |
| 315 |
Attacking Similarity-Based Link Prediction in Social Networks |
⚔Attack |
📝AAMAS |
|
2018 |
| 316 |
Hiding Individuals and Communities in a Social Network |
⚔Attack |
📝Nature Human Behavior |
|
2018 |
| 317 |
Adversarial Attacks on Neural Networks for Graph Data |
⚔Attack |
📝KDD |
Code |
2018 |
| 318 |
Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network |
⚔Attack |
📝arXiv |
|
2018 |
| 319 |
Fast Gradient Attack on Network Embedding |
⚔Attack |
📝arXiv |
|
2018 |
| 320 |
Data Poisoning Attack against Unsupervised Node Embedding Methods |
⚔Attack |
📝arXiv |
|
2018 |
| 321 |
Fake Node Attacks on Graph Convolutional Networks |
⚔Attack |
📝arXiv |
|
2018 |
| 322 |
Deep Learning on Graphs: A Survey |
📃Survey |
📝arXiv'2018 |
|
2018 |
| 323 |
Adversarial Attack and Defense on Graph Data: A Survey |
📃Survey |
📝arXiv'2018 |
|
2018 |
| 324 |
Adversarial Personalized Ranking for Recommendation |
🛡Defense |
📝SIGIR |
Code |
2018 |
| 325 |
Adversarial Sets for Regularising Neural Link Predictors |
⚔Attack |
📝UAI |
Code |
2017 |
| 326 |
Practical Attacks Against Graph-based Clustering |
⚔Attack |
📝CCS |
|
2017 |