0 |
Revisiting Graph Adversarial Attack and Defense From a Data Distribution Perspective |
⚔Attack |
📝ICLR |
Code |
2023 |
1 |
Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning |
⚔Attack |
📝AAAI |
Code |
2023 |
2 |
GUAP: Graph Universal Attack Through Adversarial Patching |
⚔Attack |
📝arXiv |
Code |
2023 |
3 |
Node Injection for Class-specific Network Poisoning |
⚔Attack |
📝arXiv |
Code |
2023 |
4 |
Unnoticeable Backdoor Attacks on Graph Neural Networks |
⚔Attack |
📝WWW |
Code |
2023 |
5 |
A semantic backdoor attack against Graph Convolutional Networks |
⚔Attack |
📝arXiv |
|
2023 |
6 |
Graph Adversarial Immunization for Certifiable Robustness |
🔐Certification |
📝arXiv'2023 |
|
2023 |
7 |
Localized Randomized Smoothing for Collective Robustness Certification |
🔐Certification |
📝ICLR'2023 |
|
2023 |
8 |
(Provable) Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More |
🔐Certification |
📝NeurIPS'2023 |
Code |
2023 |
9 |
Hierarchical Randomized Smoothing |
🔐Certification |
📝NeurIPS'2023 |
Code |
2023 |
10 |
Evaluating Robustness and Uncertainty of Graph Models Under Structural Distributional Shifts |
🚀Others |
📝arXiv‘2023 |
Code |
2023 |
11 |
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions |
🛡Defense |
📝NeurIPS |
Code |
2023 |
12 |
ASGNN: Graph Neural Networks with Adaptive Structure |
🛡Defense |
📝ICLR OpenReview |
|
2023 |
13 |
Empowering Graph Representation Learning with Test-Time Graph Transformation |
🛡Defense |
📝ICLR |
Code |
2023 |
14 |
Robust Training of Graph Neural Networks via Noise Governance |
🛡Defense |
📝WSDM |
Code |
2023 |
15 |
Self-Supervised Graph Structure Refinement for Graph Neural Networks |
🛡Defense |
📝WSDM |
Code |
2023 |
16 |
Revisiting Robustness in Graph Machine Learning |
🛡Defense |
📝ICLR |
Code |
2023 |
17 |
Robust Mid-Pass Filtering Graph Convolutional Networks |
🛡Defense |
📝WWW |
|
2023 |
18 |
Towards Robust Graph Neural Networks via Adversarial Contrastive Learning |
🛡Defense |
📝BigData |
|
2023 |
19 |
AdverSparse: An Adversarial Attack Framework for Deep Spatial-Temporal Graph Neural Networks |
⚔Attack |
📝ICASSP |
|
2022 |
20 |
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks |
⚔Attack |
📝WSDM |
|
2022 |
21 |
Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors |
⚔Attack |
📝IJCAI |
Code |
2022 |
22 |
Label-Only Membership Inference Attack against Node-Level Graph Neural NetworksCluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors |
⚔Attack |
📝arXiv |
|
2022 |
23 |
Adversarial Camouflage for Node Injection Attack on Graphs |
⚔Attack |
📝arXiv |
|
2022 |
24 |
Are Gradients on Graph Structure Reliable in Gray-box Attacks? |
⚔Attack |
📝CIKM |
Code |
2022 |
25 |
Graph Structural Attack by Perturbing Spectral Distance |
⚔Attack |
📝KDD |
|
2022 |
26 |
What Does the Gradient Tell When Attacking the Graph Structure |
⚔Attack |
📝arXiv |
|
2022 |
27 |
Label specificity attack: Change your label as I want |
⚔Attack |
📝IJIS |
|
2022 |
28 |
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection |
⚔Attack |
📝ICDM |
Code |
2022 |
29 |
Sparse Vicious Attacks on Graph Neural Networks |
⚔Attack |
📝arXiv |
Code |
2022 |
30 |
Poisoning GNN-based Recommender Systems with Generative Surrogate-based Attacks |
⚔Attack |
📝ACM TIS |
|
2022 |
31 |
Membership Inference Attacks Against Robust Graph Neural Network |
⚔Attack |
📝CSS |
|
2022 |
32 |
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks |
⚔Attack |
📝ICDM |
Code |
2022 |
33 |
Revisiting Item Promotion in GNN-based Collaborative Filtering: A Masked Targeted Topological Attack Perspective |
⚔Attack |
📝arXiv |
|
2022 |
34 |
Link-Backdoor: Backdoor Attack on Link Prediction via Node Injection |
⚔Attack |
📝arXiv |
Code |
2022 |
35 |
Private Graph Extraction via Feature Explanations |
⚔Attack |
📝arXiv |
|
2022 |
36 |
Model Inversion Attacks against Graph Neural Networks |
⚔Attack |
📝TKDE |
|
2022 |
37 |
Towards Secrecy-Aware Attacks Against Trust Prediction in Signed Graphs |
⚔Attack |
📝arXiv |
|
2022 |
38 |
Adversarial Robustness of Graph-based Anomaly Detection |
⚔Attack |
📝arXiv |
|
2022 |
39 |
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees |
⚔Attack |
📝CVPR |
Code |
2022 |
40 |
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem |
⚔Attack |
📝WSDM |
Code |
2022 |
41 |
Inference Attacks Against Graph Neural Networks |
⚔Attack |
📝USENIX Security |
Code |
2022 |
42 |
Model Stealing Attacks Against Inductive Graph Neural Networks |
⚔Attack |
📝IEEE Symposium on Security and Privacy |
Code |
2022 |
43 |
Transferable Graph Backdoor Attack |
⚔Attack |
📝RAID |
Code |
2022 |
44 |
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation |
⚔Attack |
📝WWW |
Code |
2022 |
45 |
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability |
⚔Attack |
📝ICLR |
Code |
2022 |
46 |
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs |
⚔Attack |
📝AAAI |
Code |
2022 |
47 |
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2022 |
48 |
Black-box Node Injection Attack for Graph Neural Networks |
⚔Attack |
📝arXiv |
Code |
2022 |
49 |
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection |
⚔Attack |
📝arXiv |
|
2022 |
50 |
Projective Ranking-based GNN Evasion Attacks |
⚔Attack |
📝arXiv |
|
2022 |
51 |
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation |
⚔Attack |
📝arXiv |
|
2022 |
52 |
Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization |
⚔Attack |
📝Asia CCS |
Code |
2022 |
53 |
Neighboring Backdoor Attacks on Graph Convolutional Network |
⚔Attack |
📝arXiv |
Code |
2022 |
54 |
Camouflaged Poisoning Attack on Graph Neural Networks |
⚔Attack |
📝ICDM |
|
2022 |
55 |
Dealing with the unevenness: deeper insights in graph-based attack and defense |
⚔Attack |
📝Machine Learning |
|
2022 |
56 |
Adversarial for Social Privacy: A Poisoning Strategy to Degrade User Identity Linkage |
⚔Attack |
📝arXiv |
|
2022 |
57 |
LOKI: A Practical Data Poisoning Attack Framework against Next Item Recommendations |
⚔Attack |
📝TKDE |
|
2022 |
58 |
Exploratory Adversarial Attacks on Graph Neural Networks for Semi-Supervised Node Classification |
⚔Attack |
📝Pattern Recognition |
|
2022 |
59 |
Motif-Backdoor: Rethinking the Backdoor Attack on Graph Neural Networks via Motifs |
⚔Attack |
📝arXiv |
|
2022 |
60 |
Are Defenses for Graph Neural Networks Robust? |
⚔Attack |
📝NeurIPS |
Code |
2022 |
61 |
Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation |
⚔Attack |
📝ECCV |
|
2022 |
62 |
Imperceptible Adversarial Attacks on Discrete-Time Dynamic Graph Models |
⚔Attack |
📝NeurIPS |
|
2022 |
63 |
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias |
⚔Attack |
📝NeurIPS |
Code |
2022 |
64 |
Adversary for Social Good: Leveraging Attribute-Obfuscating Attack to Protect User Privacy on Social Networks |
⚔Attack |
📝SecureComm |
|
2022 |
65 |
GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections |
⚔Attack |
📝arXiv |
Code |
2022 |
66 |
Stability and Generalization Capabilities of Message Passing Graph Neural Networks |
⚖Stability |
📝arXiv'2022 |
|
2022 |
67 |
On the Prediction Instability of Graph Neural Networks |
⚖Stability |
📝arXiv'2022 |
|
2022 |
68 |
GreatX: A graph reliability toolbox based on PyTorch and PyTorch Geometric |
⚙Toolbox |
📝arXiv’2022 |
GreatX |
2022 |
69 |
Trustworthy Graph Neural Networks: Aspects, Methods and Trends |
📃Survey |
📝arXiv'2022 |
|
2022 |
70 |
Graph Vulnerability and Robustness: A Survey |
📃Survey |
📝TKDE'2022 |
|
2022 |
71 |
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability |
📃Survey |
📝arXiv'2022 |
|
2022 |
72 |
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection |
📃Survey |
📝arXiv'2022 |
|
2022 |
73 |
A Comparative Study on Robust Graph Neural Networks to Structural Noises |
📃Survey |
📝AAAI DLG'2022 |
|
2022 |
74 |
Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack |
📃Survey |
📝arXiv'2022 |
|
2022 |
75 |
Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks |
🔐Certification |
📝NeurIPS'2022 |
Code |
2022 |
76 |
A Systematic Evaluation of Node Embedding Robustness |
🚀Others |
📝LoG‘2022 |
Code |
2022 |
77 |
We Cannot Guarantee Safety: The Undecidability of Graph Neural Network Verification |
🚀Others |
📝arXiv'2022 |
|
2022 |
78 |
Exploring High-Order Structure for Robust Graph Structure Learning |
🛡Defense |
📝arXiv |
|
2022 |
79 |
Unsupervised Adversarially-Robust Representation Learning on Graphs |
🛡Defense |
📝AAAI |
Code |
2022 |
80 |
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels |
🛡Defense |
📝WSDM |
Code |
2022 |
81 |
Mind Your Solver! On Adversarial Attack and Defense for Combinatorial Optimization |
🛡Defense |
📝arXiv |
Code |
2022 |
82 |
Learning Robust Representation through Graph Adversarial Contrastive Learning |
🛡Defense |
📝arXiv |
|
2022 |
83 |
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2022 |
84 |
Graph Neural Network for Local Corruption Recovery |
🛡Defense |
📝arXiv |
Code |
2022 |
85 |
How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks? |
🛡Defense |
📝Neural Processing Letters |
|
2022 |
86 |
Robust Heterogeneous Graph Neural Networks against Adversarial Attacks |
🛡Defense |
📝AAAI |
|
2022 |
87 |
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation |
🛡Defense |
📝WWW |
Code |
2022 |
88 |
Robust Graph Representation Learning via Predictive Coding |
🛡Defense |
📝arXiv |
|
2022 |
89 |
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets |
🛡Defense |
📝LoG |
Code |
2022 |
90 |
On the Vulnerability of Graph Learning based Collaborative Filtering |
🛡Defense |
📝TIS |
|
2022 |
91 |
Spectral Adversarial Training for Robust Graph Neural Network |
🛡Defense |
📝TKDE |
Code |
2022 |
92 |
Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation |
🛡Defense |
📝ECML-PKDD |
|
2022 |
93 |
GUARD: Graph Universal Adversarial Defense |
🛡Defense |
📝arXiv |
Code |
2022 |
94 |
Detecting Topology Attacks against Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2022 |
95 |
LPGNet: Link Private Graph Networks for Node Classification |
🛡Defense |
📝arXiv |
|
2022 |
96 |
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2022 |
97 |
Bayesian Robust Graph Contrastive Learning |
🛡Defense |
📝arXiv |
Code |
2022 |
98 |
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN |
🛡Defense |
📝KDD |
Code |
2022 |
99 |
Robust Graph Representation Learning for Local Corruption Recovery |
🛡Defense |
📝ICML workshop |
|
2022 |
100 |
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond |
🛡Defense |
📝CVPR |
Code |
2022 |
101 |
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks |
🛡Defense |
📝arXiv |
|
2022 |
102 |
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision |
🛡Defense |
📝AAAI |
Code |
2022 |
103 |
AN-GCN: An Anonymous Graph Convolutional Network Against Edge-Perturbing Attacks |
🛡Defense |
📝IEEE TNNLS |
|
2022 |
104 |
How does Heterophily Impact Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications |
🛡Defense |
📝KDD |
Code |
2022 |
105 |
Robust Graph Neural Networks using Weighted Graph Laplacian |
🛡Defense |
📝SPCOM |
Code |
2022 |
106 |
ARIEL: Adversarial Graph Contrastive Learning |
🛡Defense |
📝arXiv*· |
|
2022 |
107 |
Robust Tensor Graph Convolutional Networks via T-SVD based Graph Augmentation |
🛡Defense |
📝KDD |
Code |
2022 |
108 |
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs |
🛡Defense |
📝arXiv |
|
2022 |
109 |
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation |
🛡Defense |
📝CIKM |
Code |
2022 |
110 |
On the Robustness of Graph Neural Diffusion to Topology Perturbations |
🛡Defense |
📝NeurIPS |
Code |
2022 |
111 |
IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense |
🛡Defense |
📝IEEE IOT |
|
2022 |
112 |
Robust cross-network node classification via constrained graph mutual information |
🛡Defense |
📝KBS |
|
2022 |
113 |
Defending Against Backdoor Attack on Graph Nerual Network by Explainability |
🛡Defense |
📝arXiv |
|
2022 |
114 |
Towards an Optimal Asymmetric Graph Structure for Robust Semi-supervised Node Classification |
🛡Defense |
📝KDD |
|
2022 |
115 |
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification |
🛡Defense |
📝arXiv |
|
2022 |
116 |
Robust Graph Neural Networks via Ensemble Learning |
🛡Defense |
📝Mathematics |
|
2022 |
117 |
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification |
⚔Attack |
📝arXiv |
|
2021 |
118 |
How Members of Covert Networks Conceal the Identities of Their Leaders |
⚔Attack |
📝ACM TIST |
|
2021 |
119 |
Spatially Focused Attack against Spatiotemporal Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
120 |
Derivative-free optimization adversarial attacks for graph convolutional networks |
⚔Attack |
📝PeerJ |
|
2021 |
121 |
Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks |
⚔Attack |
📝CIKM |
|
2021 |
122 |
Time-aware Gradient Attack on Dynamic Network Link Prediction |
⚔Attack |
📝TKDE |
|
2021 |
123 |
Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning |
⚔Attack |
📝arXiv |
|
2021 |
124 |
Watermarking Graph Neural Networks based on Backdoor Attacks |
⚔Attack |
📝arXiv |
|
2021 |
125 |
Robustness of Graph Neural Networks at Scale |
⚔Attack |
📝NeurIPS |
Code |
2021 |
126 |
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness |
⚔Attack |
📝NeurIPS |
|
2021 |
127 |
Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models |
⚔Attack |
📝IJCAI |
Code |
2021 |
128 |
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods |
⚔Attack |
📝EMNLP |
Code |
2021 |
129 |
COREATTACK: Breaking Up the Core Structure of Graphs |
⚔Attack |
📝arXiv |
|
2021 |
130 |
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction |
⚔Attack |
📝ICCAD |
Code |
2021 |
131 |
GraphMI: Extracting Private Graph Data from Graph Neural Networks |
⚔Attack |
📝IJCAI |
Code |
2021 |
132 |
Structural Attack against Graph Based Android Malware Detection |
⚔Attack |
📝CCS |
|
2021 |
133 |
Adversarial Attack against Cross-lingual Knowledge Graph Alignment |
⚔Attack |
📝EMNLP |
|
2021 |
134 |
FHA: Fast Heuristic Attack Against Graph Convolutional Networks |
⚔Attack |
📝ICDS |
|
2021 |
135 |
Task and Model Agnostic Adversarial Attack on Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
136 |
Adversarial Attacks on Graph Classification via Bayesian Optimisation |
⚔Attack |
📝NeurIPS |
Code |
2021 |
137 |
Single Node Injection Attack against Graph Neural Networks |
⚔Attack |
📝CIKM |
Code |
2021 |
138 |
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking |
⚔Attack |
📝DATE Conference |
|
2021 |
139 |
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications |
⚔Attack |
📝ICDM |
Code |
2021 |
140 |
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns |
⚔Attack |
📝ACL |
Code |
2021 |
141 |
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks |
⚔Attack |
📝CCS |
|
2021 |
142 |
SAGE: Intrusion Alert-driven Attack Graph Extractor |
⚔Attack |
📝KDD Workshop |
Code |
2021 |
143 |
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models |
⚔Attack |
📝arXiv |
Code |
2021 |
144 |
VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning |
⚔Attack |
📝PAKDD |
Code |
2021 |
145 |
Explainability-based Backdoor Attacks Against Graph Neural Networks |
⚔Attack |
📝WiseML@WiSec |
|
2021 |
146 |
GraphAttacker: A General Multi-Task GraphAttack Framework |
⚔Attack |
📝arXiv |
Code |
2021 |
147 |
Attacking Graph Neural Networks at Scale |
⚔Attack |
📝AAAI workshop |
|
2021 |
148 |
Reinforcement Learning For Data Poisoning on Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
149 |
Universal Spectral Adversarial Attacks for Deformable Shapes |
⚔Attack |
📝CVPR |
|
2021 |
150 |
DeHiB: Deep Hidden Backdoor Attack on Semi-Supervised Learning via Adversarial Perturbation |
⚔Attack |
📝AAAI |
|
2021 |
151 |
Towards Revealing Parallel Adversarial Attack on Politician Socialnet of Graph Structure |
⚔Attack |
📝Security and Communication Networks |
|
2021 |
152 |
Network Embedding Attack: An Euclidean Distance Based Method |
⚔Attack |
📝MDATA |
|
2021 |
153 |
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation |
⚔Attack |
📝arXiv |
|
2021 |
154 |
Jointly Attacking Graph Neural Network and its Explanations |
⚔Attack |
📝arXiv |
|
2021 |
155 |
Graph Stochastic Neural Networks for Semi-supervised Learning |
⚔Attack |
📝arXiv |
Code |
2021 |
156 |
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings |
⚔Attack |
📝arXiv |
Code |
2021 |
157 |
Single-Node Attack for Fooling Graph Neural Networks |
⚔Attack |
📝KDD Workshop |
Code |
2021 |
158 |
The Robustness of Graph k-shell Structure under Adversarial Attacks |
⚔Attack |
📝arXiv |
|
2021 |
159 |
Graphfool: Targeted Label Adversarial Attack on Graph Embedding |
⚔Attack |
📝arXiv |
|
2021 |
160 |
Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
161 |
Node-Level Membership Inference Attacks Against Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
162 |
Adversarial Attack on Large Scale Graph |
⚔Attack |
📝TKDE |
Code |
2021 |
163 |
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense |
⚔Attack |
📝arXiv |
|
2021 |
164 |
Stealing Links from Graph Neural Networks |
⚔Attack |
📝USENIX Security |
|
2021 |
165 |
Structack: Structure-based Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝ACM Hypertext |
Code |
2021 |
166 |
Optimal Edge Weight Perturbations to Attack Shortest Paths |
⚔Attack |
📝arXiv |
|
2021 |
167 |
GReady for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack |
⚔Attack |
📝Information Sciences |
|
2021 |
168 |
Graph Adversarial Attack via Rewiring |
⚔Attack |
📝KDD |
Code |
2021 |
169 |
Membership Inference Attack on Graph Neural Networks |
⚔Attack |
📝arXiv |
|
2021 |
170 |
Graph Backdoor |
⚔Attack |
📝USENIX Security |
|
2021 |
171 |
TDGIA: Effective Injection Attacks on Graph Neural Networks |
⚔Attack |
📝KDD |
Code |
2021 |
172 |
Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge |
⚔Attack |
📝arXiv |
|
2021 |
173 |
PATHATTACK: Attacking Shortest Paths in Complex Networks |
⚔Attack |
📝arXiv |
|
2021 |
174 |
Towards a Unified Framework for Fair and Stable Graph Representation Learning |
⚖Stability |
📝UAI'2021 |
Code |
2021 |
175 |
Training Stable Graph Neural Networks Through Constrained Learning |
⚖Stability |
📝arXiv'2021 |
|
2021 |
176 |
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data |
⚖Stability |
📝NeurIPS'2021 |
Code |
2021 |
177 |
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations |
⚖Stability |
📝arXiv'2021 |
|
2021 |
178 |
DeepRobust: a Platform for Adversarial Attacks and Defenses |
⚙Toolbox |
📝AAAI’2021 |
DeepRobust |
2021 |
179 |
Evaluating Graph Vulnerability and Robustness using TIGER |
⚙Toolbox |
📝arXiv‘2021 |
TIGER |
2021 |
180 |
Graph Robustness Benchmark: Rethinking and Benchmarking Adversarial Robustness of Graph Neural Networks |
⚙Toolbox |
📝NeurIPS'2021 |
Graph Robustness Benchmark (GRB) |
2021 |
181 |
Deep Graph Structure Learning for Robust Representations: A Survey |
📃Survey |
📝arXiv'2021 |
|
2021 |
182 |
Robustness of deep learning models on graphs: A survey |
📃Survey |
📝AI Open'2021 |
|
2021 |
183 |
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies |
📃Survey |
📝SIGKDD Explorations'2021 |
|
2021 |
184 |
Graph Neural Networks Methods, Applications, and Opportunities |
📃Survey |
📝arXiv'2021 |
|
2021 |
185 |
Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning |
🔐Certification |
📝ICLR OpenReview'2021 |
|
2021 |
186 |
Robust Certification for Laplace Learning on Geometric Graphs |
🔐Certification |
📝MSML’2021 |
|
2021 |
187 |
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation |
🔐Certification |
📝KDD'2021 |
Code |
2021 |
188 |
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks |
🔐Certification |
📝ICLR'2021 |
Code |
2021 |
189 |
Adversarial Immunization for Improving Certifiable Robustness on Graphs |
🔐Certification |
📝WSDM'2021 |
|
2021 |
190 |
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks |
🚀Others |
📝arXiv'2021 |
|
2021 |
191 |
SIGL: Securing Software Installations Through Deep Graph Learning |
🚀Others |
📝USENIX'2021 |
|
2021 |
192 |
Learning to Drop: Robust Graph Neural Network via Topological Denoising |
🛡Defense |
📝WSDM |
Code |
2021 |
193 |
How effective are Graph Neural Networks in Fraud Detection for Network Data? |
🛡Defense |
📝arXiv |
|
2021 |
194 |
Graph Sanitation with Application to Node Classification |
🛡Defense |
📝arXiv |
|
2021 |
195 |
Understanding Structural Vulnerability in Graph Convolutional Networks |
🛡Defense |
📝IJCAI |
Code |
2021 |
196 |
A Robust and Generalized Framework for Adversarial Graph Embedding |
🛡Defense |
📝arXiv |
Code |
2021 |
197 |
Integrated Defense for Resilient Graph Matching |
🛡Defense |
📝ICML |
|
2021 |
198 |
Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs |
🛡Defense |
📝ICASSP |
|
2021 |
199 |
Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination |
🛡Defense |
📝WWW |
|
2021 |
200 |
Information Obfuscation of Graph Neural Network |
🛡Defense |
📝ICML |
Code |
2021 |
201 |
Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs |
🛡Defense |
📝arXiv |
|
2021 |
202 |
DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs |
🛡Defense |
📝ECML |
|
2021 |
203 |
Elastic Graph Neural Networks |
🛡Defense |
📝ICML |
Code |
2021 |
204 |
Robust Counterfactual Explanations on Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2021 |
205 |
Node Similarity Preserving Graph Convolutional Networks |
🛡Defense |
📝WSDM |
Code |
2021 |
206 |
Enhancing Robustness and Resilience of Multiplex Networks Against Node-Community Cascading Failures |
🛡Defense |
📝IEEE TSMC |
|
2021 |
207 |
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data |
🛡Defense |
📝TKDE |
Code |
2021 |
208 |
Robust Graph Learning Under Wasserstein Uncertainty |
🛡Defense |
📝arXiv |
|
2021 |
209 |
Towards Robust Graph Contrastive Learning |
🛡Defense |
📝arXiv |
|
2021 |
210 |
Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks |
🛡Defense |
📝ICML |
|
2021 |
211 |
UAG: Uncertainty-Aware Attention Graph Neural Network for Defending Adversarial Attacks |
🛡Defense |
📝AAAI |
|
2021 |
212 |
On Generalization of Graph Autoencoders with Adversarial Training |
🛡Defense |
📝ECML |
|
2021 |
213 |
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks |
🛡Defense |
📝AAAI |
|
2021 |
214 |
Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering |
🛡Defense |
📝AAAI |
Code |
2021 |
215 |
Personalized privacy protection in social networks through adversarial modeling |
🛡Defense |
📝AAAI |
|
2021 |
216 |
Interpretable Stability Bounds for Spectral Graph Filters |
🛡Defense |
📝arXiv |
|
2021 |
217 |
Graph Neural Networks with Feature and Structure Aware Random Walk |
🛡Defense |
📝arXiv |
|
2021 |
218 |
Topological Relational Learning on Graphs |
🛡Defense |
📝NeurIPS |
Code |
2021 |
219 |
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification |
🛡Defense |
📝NeurIPS |
Code |
2021 |
220 |
Graph-based Adversarial Online Kernel Learning with Adaptive Embedding |
🛡Defense |
📝ICDM |
|
2021 |
221 |
Robust Graph Neural Networks via Probabilistic Lipschitz Constraints |
🛡Defense |
📝arXiv |
|
2021 |
222 |
Randomized Generation of Adversary-Aware Fake Knowledge Graphs to Combat Intellectual Property Theft |
🛡Defense |
📝AAAI |
|
2021 |
223 |
Unified Robust Training for Graph NeuralNetworks against Label Noise |
🛡Defense |
📝arXiv |
|
2021 |
224 |
An Introduction to Robust Graph Convolutional Networks |
🛡Defense |
📝arXiv |
|
2021 |
225 |
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System |
🛡Defense |
📝arXiv |
|
2021 |
226 |
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks |
🛡Defense |
📝arXiv |
|
2021 |
227 |
Robust graph convolutional networks with directional graph adversarial training |
🛡Defense |
📝Applied Intelligence |
|
2021 |
228 |
Detection and Defense of Topological Adversarial Attacks on Graphs |
🛡Defense |
📝AISTATS |
|
2021 |
229 |
Unveiling the potential of Graph Neural Networks for robust Intrusion Detection |
🛡Defense |
📝arXiv |
Code |
2021 |
230 |
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction |
🛡Defense |
📝arXiv |
|
2021 |
231 |
EGC2: Enhanced Graph Classification with Easy Graph Compression |
🛡Defense |
📝arXiv |
|
2021 |
232 |
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis |
🛡Defense |
📝arXiv |
|
2021 |
233 |
Structure-Aware Hierarchical Graph Pooling using Information Bottleneck |
🛡Defense |
📝IJCNN |
|
2021 |
234 |
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights |
🛡Defense |
📝arXiv |
|
2021 |
235 |
CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph |
🛡Defense |
📝arXiv |
|
2021 |
236 |
Releasing Graph Neural Networks with Differential Privacy Guarantees |
🛡Defense |
📝arXiv |
|
2021 |
237 |
Speedup Robust Graph Structure Learning with Low-Rank Information |
🛡Defense |
📝CIKM |
|
2021 |
238 |
A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks |
🛡Defense |
📝ICICS |
Code |
2021 |
239 |
Node Feature Kernels Increase Graph Convolutional Network Robustness |
🛡Defense |
📝arXiv |
Code |
2021 |
240 |
On the Relationship between Heterophily and Robustness of Graph Neural Networks |
🛡Defense |
📝arXiv |
|
2021 |
241 |
Distributionally Robust Semi-Supervised Learning Over Graphs |
🛡Defense |
📝ICLR |
|
2021 |
242 |
Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation |
🛡Defense |
📝arXiv |
|
2021 |
243 |
Not All Low-Pass Filters are Robust in Graph Convolutional Networks |
🛡Defense |
📝NeurIPS |
Code |
2021 |
244 |
Towards Robust Reasoning over Knowledge Graphs |
🛡Defense |
📝arXiv |
|
2021 |
245 |
Graph Neural Networks with Adaptive Residual |
🛡Defense |
📝NeurIPS |
Code |
2021 |
246 |
Adaptive Adversarial Attack on Graph Embedding via GAN |
⚔Attack |
📝SocialSec |
|
2020 |
247 |
Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers |
⚔Attack |
📝arXiv |
|
2020 |
248 |
One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting |
⚔Attack |
📝ICLR OpenReview |
|
2020 |
249 |
Near-Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem |
⚔Attack |
📝ICLR OpenReview |
|
2020 |
250 |
Adversarial Attacks on Deep Graph Matching |
⚔Attack |
📝NeurIPS |
|
2020 |
251 |
Attacking Graph-Based Classification without Changing Existing Connections |
⚔Attack |
📝ACSAC |
|
2020 |
252 |
Cross Entropy Attack on Deep Graph Infomax |
⚔Attack |
📝IEEE ISCAS |
|
2020 |
253 |
Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation |
⚔Attack |
📝ICLR |
Code |
2020 |
254 |
Towards More Practical Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝NeurIPS |
Code |
2020 |
255 |
Adversarial Label-Flipping Attack and Defense for Graph Neural Networks |
⚔Attack |
📝ICDM |
Code |
2020 |
256 |
Exploratory Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝ICDM |
Code |
2020 |
257 |
A Targeted Universal Attack on Graph Convolutional Network |
⚔Attack |
📝arXiv |
Code |
2020 |
258 |
Query-free Black-box Adversarial Attacks on Graphs |
⚔Attack |
📝arXiv |
|
2020 |
259 |
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs |
⚔Attack |
📝arXiv |
|
2020 |
260 |
Efficient Evasion Attacks to Graph Neural Networks via Influence Function |
⚔Attack |
📝arXiv |
|
2020 |
261 |
Backdoor Attacks to Graph Neural Networks |
⚔Attack |
📝SACMAT |
Code |
2020 |
262 |
Link Prediction Adversarial Attack Via Iterative Gradient Attack |
⚔Attack |
📝IEEE Trans |
|
2020 |
263 |
Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection |
⚔Attack |
📝arXiv |
|
2020 |
264 |
A Graph Matching Attack on Privacy-Preserving Record Linkage |
⚔Attack |
📝CIKM |
|
2020 |
265 |
Adversarial Attack on Hierarchical Graph Pooling Neural Networks |
⚔Attack |
📝arXiv |
|
2020 |
266 |
Adversarial Attack on Community Detection by Hiding Individuals |
⚔Attack |
📝WWW |
Code |
2020 |
267 |
Manipulating Node Similarity Measures in Networks |
⚔Attack |
📝AAMAS |
|
2020 |
268 |
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models |
⚔Attack |
📝AAAI |
Code |
2020 |
269 |
Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks |
⚔Attack |
📝BigData |
|
2020 |
270 |
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach |
⚔Attack |
📝WWW |
|
2020 |
271 |
An Efficient Adversarial Attack on Graph Structured Data |
⚔Attack |
📝IJCAI Workshop |
|
2020 |
272 |
Practical Adversarial Attacks on Graph Neural Networks |
⚔Attack |
📝ICML Workshop |
|
2020 |
273 |
Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns |
⚔Attack |
📝TKDD |
|
2020 |
274 |
Adversarial Attacks on Link Prediction Algorithms Based on Graph Neural Networks |
⚔Attack |
📝Asia CCS |
|
2020 |
275 |
Scalable Attack on Graph Data by Injecting Vicious Nodes |
⚔Attack |
📝ECML-PKDD |
Code |
2020 |
276 |
Attackability Characterization of Adversarial Evasion Attack on Discrete Data |
⚔Attack |
📝KDD |
|
2020 |
277 |
MGA: Momentum Gradient Attack on Network |
⚔Attack |
📝arXiv |
|
2020 |
278 |
Adversarial Perturbations of Opinion Dynamics in Networks |
⚔Attack |
📝arXiv |
|
2020 |
279 |
Network disruption: maximizing disagreement and polarization in social networks |
⚔Attack |
📝arXiv |
Code |
2020 |
280 |
Adversarial attack on BC classification for scale-free networks |
⚔Attack |
📝AIP Chaos |
|
2020 |
281 |
Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria |
⚔Attack |
📝arXiv |
|
2020 |
282 |
Graph and Graphon Neural Network Stability |
⚖Stability |
📝arXiv'2020 |
|
2020 |
283 |
On the Stability of Graph Convolutional Neural Networks under Edge Rewiring |
⚖Stability |
📝arXiv'2020 |
|
2020 |
284 |
Graph Neural Networks: Architectures, Stability and Transferability |
⚖Stability |
📝arXiv'2020 |
|
2020 |
285 |
Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method |
⚖Stability |
📝arXiv'2020 |
|
2020 |
286 |
Stability of Graph Neural Networks to Relative Perturbations |
⚖Stability |
📝ICASSP'2020 |
|
2020 |
287 |
Graph Neural Networks Taxonomy, Advances and Trends |
📃Survey |
📝arXiv'2020 |
|
2020 |
288 |
A Survey of Adversarial Learning on Graph |
📃Survey |
📝arXiv'2020 |
|
2020 |
289 |
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing |
🔐Certification |
📝GLOBECOM'2020 |
|
2020 |
290 |
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation |
🔐Certification |
📝KDD'2020 |
Code |
2020 |
291 |
Efficient Robustness Certificates for Discrete Data: Sparsity - Aware Randomized Smoothing for Graphs, Images and More |
🔐Certification |
📝ICML'2020 |
Code |
2020 |
292 |
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing |
🔐Certification |
📝WWW'2020 |
|
2020 |
293 |
Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks |
🔐Certification |
📝NeurIPS'2020 |
Code |
2020 |
294 |
Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning |
🔐Certification |
📝AAAI'2020 |
|
2020 |
295 |
Abstract Interpretation based Robustness Certification for Graph Convolutional Networks |
🔐Certification |
📝ECAI'2020 |
|
2020 |
296 |
When Does Self-Supervision Help Graph Convolutional Networks? |
🚀Others |
📝ICML'2020 |
|
2020 |
297 |
Training Robust Graph Neural Network by Applying Lipschitz Constant Constraint |
🚀Others |
📝CentraleSupélec'2020 |
Code |
2020 |
298 |
Watermarking Graph Neural Networks by Random Graphs |
🚀Others |
📝arXiv'2020 |
|
2020 |
299 |
FLAG: Adversarial Data Augmentation for Graph Neural Networks |
🚀Others |
📝arXiv'2020 |
Code |
2020 |
300 |
AANE: Anomaly Aware Network Embedding For Anomalous Link Detection |
🛡Defense |
📝ICDM |
|
2020 |
301 |
Provably Robust Node Classification via Low-Pass Message Passing |
🛡Defense |
📝ICDM |
|
2020 |
302 |
Graph-Revised Convolutional Network |
🛡Defense |
📝ECML-PKDD |
Code |
2020 |
303 |
Robust Training of Graph Convolutional Networks via Latent Perturbation |
🛡Defense |
📝ECML-PKDD |
|
2020 |
304 |
DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder |
🛡Defense |
📝arXiv |
Code |
2020 |
305 |
Transferring Robustness for Graph Neural Network Against Poisoning Attacks |
🛡Defense |
📝WSDM |
Code |
2020 |
306 |
All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs |
🛡Defense |
📝WSDM |
Code |
2020 |
307 |
How Robust Are Graph Neural Networks to Structural Noise? |
🛡Defense |
📝DLGMA |
|
2020 |
308 |
Robust Detection of Adaptive Spammers by Nash Reinforcement Learning |
🛡Defense |
📝KDD |
Code |
2020 |
309 |
Graph Structure Learning for Robust Graph Neural Networks |
🛡Defense |
📝KDD |
Code |
2020 |
310 |
On The Stability of Polynomial Spectral Graph Filters |
🛡Defense |
📝ICASSP |
Code |
2020 |
311 |
On the Robustness of Cascade Diffusion under Node Attacks |
🛡Defense |
📝WWW |
Code |
2020 |
312 |
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks |
🛡Defense |
📝WWW |
|
2020 |
313 |
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks |
🛡Defense |
📝ICASSP |
|
2020 |
314 |
Robust Graph Representation Learning via Neural Sparsification |
🛡Defense |
📝ICML |
|
2020 |
315 |
Robust Collective Classification against Structural Attacks |
🛡Defense |
📝Preprint |
|
2020 |
316 |
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters |
🛡Defense |
📝CIKM |
Code |
2020 |
317 |
Topological Effects on Attacks Against Vertex Classification |
🛡Defense |
📝arXiv |
|
2020 |
318 |
Tensor Graph Convolutional Networks for Multi-relational and Robust Learning |
🛡Defense |
📝arXiv |
|
2020 |
319 |
Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning |
🛡Defense |
📝arXiv |
|
2020 |
320 |
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks |
🛡Defense |
📝NeurIPS |
Code |
2020 |
321 |
Robust Graph Learning From Noisy Data |
🛡Defense |
📝IEEE Trans |
|
2020 |
322 |
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks |
🛡Defense |
📝arXiv |
|
2020 |
323 |
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach |
🛡Defense |
📝ICLR OpenReview |
|
2020 |
324 |
Provable Overlapping Community Detection in Weighted Graphs |
🛡Defense |
📝NeurIPS |
|
2020 |
325 |
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings |
🛡Defense |
📝NeurIPS |
Code |
2020 |
326 |
Graph Random Neural Networks for Semi-Supervised Learning on Graphs |
🛡Defense |
📝NeurIPS |
Code |
2020 |
327 |
Reliable Graph Neural Networks via Robust Aggregation |
🛡Defense |
📝NeurIPS |
Code |
2020 |
328 |
Towards Robust Graph Neural Networks against Label Noise |
🛡Defense |
📝ICLR OpenReview |
|
2020 |
329 |
Graph Adversarial Networks: Protecting Information against Adversarial Attacks |
🛡Defense |
📝ICLR OpenReview |
Code |
2020 |
330 |
A Novel Defending Scheme for Graph-Based Classification Against Graph Structure Manipulating Attack |
🛡Defense |
📝SocialSec |
|
2020 |
331 |
Node Copying for Protection Against Graph Neural Network Topology Attacks |
🛡Defense |
📝arXiv |
|
2020 |
332 |
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian |
🛡Defense |
📝NeurIPS |
|
2020 |
333 |
A Feature-Importance-Aware and Robust Aggregator for GCN |
🛡Defense |
📝CIKM |
Code |
2020 |
334 |
Anti-perturbation of Online Social Networks by Graph Label Transition |
🛡Defense |
📝arXiv |
|
2020 |
335 |
Graph Information Bottleneck |
🛡Defense |
📝NeurIPS |
Code |
2020 |
336 |
Adversarial Detection on Graph Structured Data |
🛡Defense |
📝PPMLP |
|
2020 |
337 |
Graph Contrastive Learning with Augmentations |
🛡Defense |
📝NeurIPS |
Code |
2020 |
338 |
Learning Graph Embedding with Adversarial Training Methods |
🛡Defense |
📝IEEE Transactions on Cybernetics |
|
2020 |
339 |
I-GCN: Robust Graph Convolutional Network via Influence Mechanism |
🛡Defense |
📝arXiv |
|
2020 |
340 |
Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks |
🛡Defense |
📝AAAI |
|
2020 |
341 |
Smoothing Adversarial Training for GNN |
🛡Defense |
📝IEEE TCSS |
|
2020 |
342 |
Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks |
🛡Defense |
📝None |
Code |
2020 |
343 |
RoGAT: a robust GNN combined revised GAT with adjusted graphs |
🛡Defense |
📝arXiv |
|
2020 |
344 |
Adversarial Privacy Preserving Graph Embedding against Inference Attack |
🛡Defense |
📝arXiv |
Code |
2020 |
345 |
Adversarial Attacks on Node Embeddings via Graph Poisoning |
⚔Attack |
📝ICML |
Code |
2019 |
346 |
GA Based Q-Attack on Community Detection |
⚔Attack |
📝TCSS |
|
2019 |
347 |
Data Poisoning Attack against Knowledge Graph Embedding |
⚔Attack |
📝IJCAI |
|
2019 |
348 |
Adversarial Attacks on Graph Neural Networks via Meta Learning |
⚔Attack |
📝ICLR |
Code |
2019 |
349 |
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective |
⚔Attack |
📝IJCAI |
Code |
2019 |
350 |
Adversarial Examples on Graph Data: Deep Insights into Attack and Defense |
⚔Attack |
📝IJCAI |
Code |
2019 |
351 |
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning |
⚔Attack |
📝NeurIPS |
Code |
2019 |
352 |
Attacking Graph-based Classification via Manipulating the Graph Structure |
⚔Attack |
📝CCS |
|
2019 |
353 |
αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model |
⚔Attack |
📝CIKM |
|
2019 |
354 |
Multiscale Evolutionary Perturbation Attack on Community Detection |
⚔Attack |
📝arXiv |
|
2019 |
355 |
PeerNets Exploiting Peer Wisdom Against Adversarial Attacks |
⚔Attack |
📝ICLR |
Code |
2019 |
356 |
Network Structural Vulnerability A Multi-Objective Attacker Perspective |
⚔Attack |
📝IEEE Trans |
|
2019 |
357 |
Attacking Graph Convolutional Networks via Rewiring |
⚔Attack |
📝arXiv |
|
2019 |
358 |
Unsupervised Euclidean Distance Attack on Network Embedding |
⚔Attack |
📝arXiv |
|
2019 |
359 |
Structured Adversarial Attack Towards General Implementation and Better Interpretability |
⚔Attack |
📝ICLR |
Code |
2019 |
360 |
Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling |
⚔Attack |
📝arXiv |
|
2019 |
361 |
Vertex Nomination, Consistent Estimation, and Adversarial Modification |
⚔Attack |
📝arXiv |
|
2019 |
362 |
Stability Properties of Graph Neural Networks |
⚖Stability |
📝arXiv'2019 |
|
2019 |
363 |
When Do GNNs Work: Understanding and Improving Neighborhood Aggregation |
⚖Stability |
📝IJCAI Workshop'2019 |
Code |
2019 |
364 |
Stability and Generalization of Graph Convolutional Neural Networks |
⚖Stability |
📝KDD'2019 |
|
2019 |
365 |
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review |
📃Survey |
📝arXiv'2019 |
|
2019 |
366 |
Certifiable Robustness to Graph Perturbations |
🔐Certification |
📝NeurIPS'2019 |
Code |
2019 |
367 |
Certifiable Robustness and Robust Training for Graph Convolutional Networks |
🔐Certification |
📝KDD'2019 |
Code |
2019 |
368 |
Perturbation Sensitivity of GNNs |
🚀Others |
📝cs224w'2019 |
|
2019 |
369 |
Bayesian graph convolutional neural networks for semi-supervised classification |
🛡Defense |
📝AAAI |
Code |
2019 |
370 |
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning |
🛡Defense |
📝arXiv |
Code |
2019 |
371 |
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique |
🛡Defense |
📝arXiv |
|
2019 |
372 |
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems |
🛡Defense |
📝arXiv |
|
2019 |
373 |
Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations |
🛡Defense |
📝arXiv |
|
2019 |
374 |
Adversarial Defense Framework for Graph Neural Network |
🛡Defense |
📝arXiv |
|
2019 |
375 |
GraphSAC: Detecting anomalies in large-scale graphs |
🛡Defense |
📝arXiv |
|
2019 |
376 |
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure |
🛡Defense |
📝TKDE |
Code |
2019 |
377 |
Edge Dithering for Robust Adaptive Graph Convolutional Networks |
🛡Defense |
📝arXiv |
|
2019 |
378 |
Can Adversarial Network Attack be Defended? |
🛡Defense |
📝arXiv |
|
2019 |
379 |
Adversarial Training Methods for Network Embedding |
🛡Defense |
📝WWW |
Code |
2019 |
380 |
GraphDefense: Towards Robust Graph Convolutional Networks |
🛡Defense |
📝arXiv |
|
2019 |
381 |
Robust Graph Data Learning via Latent Graph Convolutional Representation |
🛡Defense |
📝arXiv |
|
2019 |
382 |
Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications |
🛡Defense |
📝NAACL |
Code |
2019 |
383 |
Robust Graph Convolutional Networks Against Adversarial Attacks |
🛡Defense |
📝KDD |
Code |
2019 |
384 |
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification |
🛡Defense |
📝PRCV |
|
2019 |
385 |
Comparing and Detecting Adversarial Attacks for Graph Deep Learning |
🛡Defense |
📝RLGM@ICLR |
|
2019 |
386 |
Characterizing Malicious Edges targeting on Graph Neural Networks |
🛡Defense |
📝ICLR OpenReview |
Code |
2019 |
387 |
Latent Adversarial Training of Graph Convolution Networks |
🛡Defense |
📝LRGSD@ICML |
Code |
2019 |
388 |
Batch Virtual Adversarial Training for Graph Convolutional Networks |
🛡Defense |
📝ICML |
Code |
2019 |
389 |
Adversarial Robustness of Similarity-Based Link Prediction |
🛡Defense |
📝ICDM |
|
2019 |
390 |
Improving Robustness to Attacks Against Vertex Classification |
🛡Defense |
📝MLG@KDD |
|
2019 |
391 |
Fake Node Attacks on Graph Convolutional Networks |
⚔Attack |
📝arXiv |
|
2018 |
392 |
Fast Gradient Attack on Network Embedding |
⚔Attack |
📝arXiv |
|
2018 |
393 |
Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network |
⚔Attack |
📝arXiv |
|
2018 |
394 |
Adversarial Attacks on Neural Networks for Graph Data |
⚔Attack |
📝KDD |
Code |
2018 |
395 |
Hiding Individuals and Communities in a Social Network |
⚔Attack |
📝Nature Human Behavior |
|
2018 |
396 |
Attacking Similarity-Based Link Prediction in Social Networks |
⚔Attack |
📝AAMAS |
|
2018 |
397 |
Adversarial Attack on Graph Structured Data |
⚔Attack |
📝ICML |
Code |
2018 |
398 |
Data Poisoning Attack against Unsupervised Node Embedding Methods |
⚔Attack |
📝arXiv |
|
2018 |
399 |
Deep Learning on Graphs: A Survey |
📃Survey |
📝arXiv'2018 |
|
2018 |
400 |
Adversarial Attack and Defense on Graph Data: A Survey |
📃Survey |
📝arXiv'2018 |
|
2018 |
401 |
Adversarial Personalized Ranking for Recommendation |
🛡Defense |
📝SIGIR |
Code |
2018 |
402 |
Practical Attacks Against Graph-based Clustering |
⚔Attack |
📝CCS |
|
2017 |
403 |
Adversarial Sets for Regularising Neural Link Predictors |
⚔Attack |
📝UAI |
Code |
2017 |