Need help with Graph-Adversarial-Learning?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

gitgiter
241 Stars 37 Forks GNU General Public License v3.0 270 Commits 0 Opened issues

Description

A curated collection of adversarial attack and defense on graph data.

Services available

!
?

Need anything else?

Contributors list

⚔🛡 Awesome Graph Adversarial Learning (Updating 274 papers)

AwesomeContributions Welcome

This repository contains Attack-related papers, Defense-related papers, Robustness Certification papers, etc., ranging from 2017 to 2021. If you find this repo useful, please cite: A Survey of Adversarial Learning on Graph, Arxiv'20, Link

@article{chen2020survey,
  title={A Survey of Adversarial Learning on Graph},
  author={Chen, Liang and Li, Jintang and Peng, Jiaying and Xie, 
        Tao and Cao, Zengxu and Xu, Kun and He, Xiangnan and Zheng, Zibin},
  journal={arXiv preprint arXiv:2003.05730},
  year={2020}
}

👀Quick Look

The papers in this repo are categorized or sorted:

| By Alphabet | By Year | By Venue | Papers with Code |

If you want to get a quick look at the recently updated papers in the repository (in 30 days), you can refer to 📍this.

⚔Attack

2021

💨 Back to Top

  • Stealing Links from Graph Neural Networks, 📝USENIX Security
  • PATHATTACK: Attacking Shortest Paths in Complex Networks, 📝arXiv
  • Structack: Structure-based Adversarial Attacks on Graph Neural Networks, 📝ACM Hypertext, :octocat:Code
  • Optimal Edge Weight Perturbations to Attack Shortest Paths, 📝arXiv
  • GReady for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack, 📝Information Sciences
  • Graph Adversarial Attack via Rewiring, 📝KDD, :octocat:Code
  • Membership Inference Attack on Graph Neural Networks, 📝arXiv
  • BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection, 📝arXiv
  • Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem, 📝arXiv
  • TDGIA: Effective Injection Attacks on Graph Neural Networks, 📝KDD, :octocat:Code
  • Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge, 📝arXiv
  • Adversarial Attack on Large Scale Graph, 📝TKDE, :octocat:Code
  • Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense, 📝arXiv
  • Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks, 📝arXiv
  • Universal Spectral Adversarial Attacks for Deformable Shapes, 📝CVPR
  • SAGE: Intrusion Alert-driven Attack Graph Extractor, 📝KDD Workshop, :octocat:Code
  • Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models, 📝arXiv, :octocat:Code
  • VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning, 📝PAKDD, :octocat:Code
  • Explainability-based Backdoor Attacks Against Graph Neural Networks, 📝arXiv
  • GraphAttacker: A General Multi-Task GraphAttack Framework, 📝arXiv, :octocat:Code
  • Attacking Graph Neural Networks at Scale, 📝AAAI workshop
  • Node-Level Membership Inference Attacks Against Graph Neural Networks, 📝arXiv
  • Reinforcement Learning For Data Poisoning on Graph Neural Networks, 📝arXiv
  • Graph Backdoor, 📝USENIX Security
  • DeHiB: Deep Hidden Backdoor Attack on Semi-Supervised Learning via Adversarial Perturbation, 📝AAAI
  • Graphfool: Targeted Label Adversarial Attack on Graph Embedding, 📝arXiv
  • Towards Revealing Parallel Adversarial Attack on Politician Socialnet of Graph Structure, 📝Security and Communication Networks
  • Network Embedding Attack: An Euclidean Distance Based Method, 📝MDATA
  • Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation, 📝arXiv
  • Jointly Attacking Graph Neural Network and its Explanations, 📝arXiv
  • Graph Stochastic Neural Networks for Semi-supervised Learning, 📝arXiv, :octocat:Code
  • Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings, 📝arXiv, :octocat:Code
  • Single-Node Attack for Fooling Graph Neural Networks, 📝KDD Workshop, :octocat:Code
  • The Robustness of Graph k-shell Structure under Adversarial Attacks, 📝arXiv
  • Poisoning Knowledge Graph Embeddings via Relation Inference Patterns, 📝ACL, :octocat:Code
  • A Hard Label Black-box Adversarial Attack Against Graph Neural Networks, 📝CCS
  • GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking, 📝DATE Conference
  • Single Node Injection Attack against Graph Neural Networks, 📝CIKM, :octocat:Code
  • Spatially Focused Attack against Spatiotemporal Graph Neural Networks, 📝arXiv
  • Derivative-free optimization adversarial attacks for graph convolutional networks, 📝PeerJ
  • Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks, 📝CIKM
  • Query-based Adversarial Attacks on Graph with Fake Nodes, 📝arXiv
  • Time-aware Gradient Attack on Dynamic Network Link Prediction, 📝TKDE
  • Inference Attacks Against Graph Neural Networks, 📝USENIX Security, :octocat:Code
  • Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning, 📝arXiv

2020

💨 Back to Top

  • A Graph Matching Attack on Privacy-Preserving Record Linkage, 📝CIKM
  • Semantic-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection, 📝arXiv
  • Adaptive Adversarial Attack on Graph Embedding via GAN, 📝SocialSec
  • Scalable Adversarial Attack on Graph Neural Networks with Alternating Direction Method of Multipliers, 📝arXiv
  • One Vertex Attack on Graph Neural Networks-based Spatiotemporal Forecasting, 📝ICLR OpenReview
  • Near-Black-Box Adversarial Attacks on Graph Neural Networks as An Influence Maximization Problem, 📝ICLR OpenReview
  • Adversarial Attacks on Deep Graph Matching, 📝NeurIPS
  • Attacking Graph-Based Classification without Changing Existing Connections, 📝ACSAC
  • Cross Entropy Attack on Deep Graph Infomax, 📝IEEE ISCAS
  • Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization, 📝arXiv
  • Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation, 📝ICLR, :octocat:Code
  • Towards More Practical Adversarial Attacks on Graph Neural Networks, 📝NeurIPS, :octocat:Code
  • Adversarial Label-Flipping Attack and Defense for Graph Neural Networks, 📝ICDM, :octocat:Code
  • Exploratory Adversarial Attacks on Graph Neural Networks, 📝ICDM, :octocat:Code
  • A Targeted Universal Attack on Graph Convolutional Network, 📝arXiv, :octocat:Code
  • Query-free Black-box Adversarial Attacks on Graphs, 📝arXiv
  • An Efficient Adversarial Attack on Graph Structured Data, 📝IJCAI Workshop
  • Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs, 📝arXiv
  • Efficient Evasion Attacks to Graph Neural Networks via Influence Function, 📝arXiv
  • Backdoor Attacks to Graph Neural Networks, 📝ICLR OpenReview
  • Link Prediction Adversarial Attack Via Iterative Gradient Attack, 📝IEEE Trans
  • Adversarial Attack on Hierarchical Graph Pooling Neural Networks, 📝arXiv
  • Adversarial Attack on Community Detection by Hiding Individuals, 📝WWW, :octocat:Code
  • Manipulating Node Similarity Measures in Networks, 📝AAMAS
  • A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models, 📝AAAI, :octocat:Code
  • Indirect Adversarial Attacks via Poisoning Neighbors for Graph Convolutional Networks, 📝BigData
  • Non-target-specific Node Injection Attacks on Graph Neural Networks: A Hierarchical Reinforcement Learning Approach, 📝WWW
  • An Efficient Adversarial Attack on Graph Structured Data, 📝IJCAI Workshop
  • Practical Adversarial Attacks on Graph Neural Networks, 📝ICML Workshop
  • Adversarial Attacks on Graph Neural Networks: Perturbations and their Patterns, 📝TKDD
  • Adversarial Attacks on Link Prediction Algorithms Based on Graph Neural Networks, 📝Asia CCS
  • Scalable Attack on Graph Data by Injecting Vicious Nodes, 📝ECML-PKDD
  • Attackability Characterization of Adversarial Evasion Attack on Discrete Data, 📝KDD
  • MGA: Momentum Gradient Attack on Network, 📝arXiv
  • Adversarial Attacks to Scale-Free Networks: Testing the Robustness of Physical Criteria, 📝arXiv
  • Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models, 📝arXiv, :octocat:Code
  • Adversarial Perturbations of Opinion Dynamics in Networks, 📝arXiv
  • Network disruption: maximizing disagreement and polarization in social networks, 📝arXiv, :octocat:Code
  • Adversarial attack on BC classification for scale-free networks, 📝AIP Chaos

2019

💨 Back to Top

  • Attacking Graph Convolutional Networks via Rewiring, 📝arXiv
  • Unsupervised Euclidean Distance Attack on Network Embedding, 📝arXiv
  • Structured Adversarial Attack Towards General Implementation and Better Interpretability, 📝ICLR, :octocat:Code
  • Generalizable Adversarial Attacks with Latent Variable Perturbation Modelling, 📝arXiv
  • Vertex Nomination, Consistent Estimation, and Adversarial Modification, 📝arXiv
  • PeerNets Exploiting Peer Wisdom Against Adversarial Attacks, 📝ICLR, :octocat:Code
  • Network Structural Vulnerability A Multi-Objective Attacker Perspective, 📝IEEE Trans
  • Multiscale Evolutionary Perturbation Attack on Community Detection, 📝arXiv
  • αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model, 📝CIKM
  • Adversarial Attacks on Node Embeddings via Graph Poisoning, 📝ICML, :octocat:Code
  • GA Based Q-Attack on Community Detection, 📝TCSS
  • Data Poisoning Attack against Knowledge Graph Embedding, 📝IJCAI
  • Adversarial Attacks on Graph Neural Networks via Meta Learning, 📝ICLR, :octocat:Code
  • Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, 📝IJCAI, :octocat:Code
  • Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, 📝IJCAI, :octocat:Code
  • A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning, 📝NeurIPS, :octocat:Code
  • Attacking Graph-based Classification via Manipulating the Graph Structure, 📝CCS

2018

💨 Back to Top

  • Fake Node Attacks on Graph Convolutional Networks, 📝arXiv
  • Data Poisoning Attack against Unsupervised Node Embedding Methods, 📝arXiv
  • Fast Gradient Attack on Network Embedding, 📝arXiv
  • Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network, 📝arXiv
  • Adversarial Attacks on Neural Networks for Graph Data, 📝KDD, :octocat:Code
  • Hiding Individuals and Communities in a Social Network, 📝Nature Human Behavior
  • Attacking Similarity-Based Link Prediction in Social Networks, 📝AAMAS
  • Adversarial Attack on Graph Structured Data, 📝ICML, :octocat:Code

2017

💨 Back to Top

  • Practical Attacks Against Graph-based Clustering, 📝CCS
  • Adversarial Sets for Regularising Neural Link Predictors, 📝UAI, :octocat:Code

🛡Defense

2021

💨 Back to Top

  • Learning to Drop: Robust Graph Neural Network via Topological Denoising, 📝WSDM, :octocat:Code
  • How effective are Graph Neural Networks in Fraud Detection for Network Data?, 📝arXiv
  • Graph Sanitation with Application to Node Classification, 📝arXiv
  • Understanding Structural Vulnerability in Graph Convolutional Networks, 📝IJCAI, :octocat:Code
  • A Robust and Generalized Framework for Adversarial Graph Embedding, 📝arXiv, :octocat:Code
  • Integrated Defense for Resilient Graph Matching, 📝ICML
  • Unveiling Anomalous Nodes Via Random Sampling and Consensus on Graphs, 📝ICASSP
  • Robust Network Alignment via Attack Signal Scaling and Adversarial Perturbation Elimination, 📝WWW
  • Adversarial Graph Augmentation to Improve Graph Contrastive Learning, 📝arXiv
  • Information Obfuscation of Graph Neural Network, 📝ICML, :octocat:Code
  • Improving Robustness of Graph Neural Networks with Heterophily-Inspired Designs, 📝arXiv
  • On Generalization of Graph Autoencoders with Adversarial Training, 📝ECML
  • DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs, 📝ECML
  • Elastic Graph Neural Networks, 📝ICML, :octocat:Code
  • Robust Counterfactual Explanations on Graph Neural Networks, 📝arXiv
  • Node Similarity Preserving Graph Convolutional Networks, 📝WSDM, :octocat:Code
  • Enhancing Robustness and Resilience of Multiplex Networks Against Node-Community Cascading Failures, 📝IEEE TSMC
  • NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data, 📝TKDE, :octocat:Code
  • Robust Graph Learning Under Wasserstein Uncertainty, 📝arXiv
  • Towards Robust Graph Contrastive Learning, 📝arXiv
  • Expressive 1-Lipschitz Neural Networks for Robust Multiple Graph Learning against Adversarial Attacks, 📝ICML
  • UAG: Uncertainty-Aware Attention Graph Neural Network for Defending Adversarial Attacks, 📝AAAI
  • Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks, 📝AAAI
  • Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering, 📝AAAI, :octocat:Code
  • Personalized privacy protection in social networks through adversarial modeling, 📝AAAI
  • Interpretable Stability Bounds for Spectral Graph Filters, 📝arXiv
  • Randomized Generation of Adversary-Aware Fake Knowledge Graphs to Combat Intellectual Property Theft, 📝AAAI
  • Unified Robust Training for Graph NeuralNetworks against Label Noise, 📝arXiv
  • An Introduction to Robust Graph Convolutional Networks, 📝arXiv
  • E-GraphSAGE: A Graph Neural Network based Intrusion Detection System, 📝arXiv
  • Spatio-Temporal Sparsification for General Robust Graph Convolution Networks, 📝arXiv
  • Robust graph convolutional networks with directional graph adversarial training, 📝Applied Intelligence
  • Detection and Defense of Topological Adversarial Attacks on Graphs, 📝AISTATS
  • Unveiling the potential of Graph Neural Networks for robust Intrusion Detection, 📝arXiv, :octocat:Code
  • Adversarial Robustness of Probabilistic Network Embedding for Link Prediction, 📝arXiv
  • EGC2: Enhanced Graph Classification with Easy Graph Compression, 📝arXiv
  • LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis, 📝arXiv
  • Structure-Aware Hierarchical Graph Pooling using Information Bottleneck, 📝IJCNN
  • Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights, 📝arXiv
  • CoG: a Two-View Co-training Framework for Defending Adversarial Attacks on Graph, 📝arXiv
  • Releasing Graph Neural Networks with Differential Privacy Guarantees, 📝arXiv
  • Speedup Robust Graph Structure Learning with Low-Rank Information, 📝CIKM
  • A Lightweight Metric Defence Strategy for Graph Neural Networks Against Poisoning Attacks, 📝ICICS, :octocat:Code
  • Node Feature Kernels Increase Graph Convolutional Network Robustness, 📝arXiv, :octocat:Code
  • Robust Graph Data Learning via Latent Graph Convolutional Representation, 📝arXiv

2020

💨 Back to Top

  • Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach, 📝ICLR OpenReview
  • Provable Overlapping Community Detection in Weighted Graphs, 📝NeurIPS
  • Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings, 📝NeurIPS, :octocat:Code
  • Graph Random Neural Networks for Semi-Supervised Learning on Graphs, 📝NeurIPS, :octocat:Code
  • Reliable Graph Neural Networks via Robust Aggregation, 📝NeurIPS, :octocat:Code
  • Towards Robust Graph Neural Networks against Label Noise, 📝ICLR OpenReview
  • Graph Adversarial Networks: Protecting Information against Adversarial Attacks, 📝ICLR OpenReview, :octocat:Code
  • A Novel Defending Scheme for Graph-Based Classification Against Graph Structure Manipulating Attack, 📝SocialSec
  • Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings, 📝NeurIPS, :octocat:Code
  • Node Copying for Protection Against Graph Neural Network Topology Attacks, 📝arXiv
  • Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian, 📝NeurIPS
  • Unsupervised Adversarially-Robust Representation Learning on Graphs, 📝arXiv
  • A Feature-Importance-Aware and Robust Aggregator for GCN, 📝CIKM, :octocat:Code
  • Anti-perturbation of Online Social Networks by Graph Label Transition, 📝arXiv
  • Graph Information Bottleneck, 📝NeurIPS, :octocat:Code
  • Adversarial Detection on Graph Structured Data, 📝PPMLP
  • Graph Contrastive Learning with Augmentations, 📝NeurIPS, :octocat:Code
  • Learning Graph Embedding with Adversarial Training Methods, 📝IEEE Transactions on Cybernetics
  • I-GCN: Robust Graph Convolutional Network via Influence Mechanism, 📝arXiv
  • Adversary for Social Good: Protecting Familial Privacy through Joint Adversarial Attacks, 📝AAAI
  • Smoothing Adversarial Training for GNN, 📝IEEE TCSS
  • Graph Structure Reshaping Against Adversarial Attacks on Graph Neural Networks, 📝None, :octocat:Code
  • RoGAT: a robust GNN combined revised GAT with adjusted graphs, 📝arXiv
  • ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks, 📝arXiv
  • Adversarial Perturbations of Opinion Dynamics in Networks, 📝arXiv
  • Adversarial Privacy Preserving Graph Embedding against Inference Attack, 📝arXiv, :octocat:Code
  • Robust Graph Learning From Noisy Data, 📝IEEE Trans
  • GNNGuard: Defending Graph Neural Networks against Adversarial Attacks, 📝NeurIPS, :octocat:Code
  • Transferring Robustness for Graph Neural Network Against Poisoning Attacks, 📝WSDM, :octocat:Code
  • All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs, 📝WSDM, :octocat:Code
  • How Robust Are Graph Neural Networks to Structural Noise?, 📝DLGMA
  • Robust Detection of Adaptive Spammers by Nash Reinforcement Learning, 📝KDD, :octocat:Code
  • Graph Structure Learning for Robust Graph Neural Networks, 📝KDD, :octocat:Code
  • On The Stability of Polynomial Spectral Graph Filters, 📝ICASSP, :octocat:Code
  • On the Robustness of Cascade Diffusion under Node Attacks, 📝WWW, :octocat:Code
  • Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks, 📝WWW
  • Towards an Efficient and General Framework of Robust Training for Graph Neural Networks, 📝ICASSP
  • Robust Graph Representation Learning via Neural Sparsification, 📝ICML
  • Robust Training of Graph Convolutional Networks via Latent Perturbation, 📝ECML-PKDD
  • Robust Collective Classification against Structural Attacks, 📝Preprint
  • Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters, 📝CIKM, :octocat:Code
  • Topological Effects on Attacks Against Vertex Classification, 📝arXiv
  • Tensor Graph Convolutional Networks for Multi-relational and Robust Learning, 📝arXiv
  • DefenseVGAE: Defending against Adversarial Attacks on Graph Data via a Variational Graph Autoencoder, 📝arXiv, :octocat:Code
  • Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning, 📝arXiv
  • AANE: Anomaly Aware Network Embedding For Anomalous Link Detection, 📝ICDM
  • Provably Robust Node Classification via Low-Pass Message Passing, 📝ICDM

2019

💨 Back to Top

  • Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure, 📝TKDE, :octocat:Code
  • Bayesian graph convolutional neural networks for semi-supervised classification, 📝AAAI, :octocat:Code
  • Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations, 📝arXiv
  • Examining Adversarial Learning against Graph-based IoT Malware Detection Systems, 📝arXiv
  • Adversarial Embedding: A robust and elusive Steganography and Watermarking technique, 📝arXiv
  • Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning, 📝arXiv, :octocat:Code
  • Adversarial Defense Framework for Graph Neural Network, 📝arXiv
  • GraphSAC: Detecting anomalies in large-scale graphs, 📝arXiv
  • Edge Dithering for Robust Adaptive Graph Convolutional Networks, 📝arXiv
  • Can Adversarial Network Attack be Defended?, 📝arXiv
  • GraphDefense: Towards Robust Graph Convolutional Networks, 📝arXiv
  • Adversarial Training Methods for Network Embedding, 📝WWW, :octocat:Code
  • Adversarial Examples on Graph Data: Deep Insights into Attack and Defense, 📝IJCAI, :octocat:Code
  • Improving Robustness to Attacks Against Vertex Classification, 📝[email protected]
  • Adversarial Robustness of Similarity-Based Link Prediction, 📝ICDM
  • αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model, 📝CIKM
  • Batch Virtual Adversarial Training for Graph Convolutional Networks, 📝ICML, :octocat:Code
  • Latent Adversarial Training of Graph Convolution Networks, 📝[email protected], :octocat:Code
  • Characterizing Malicious Edges targeting on Graph Neural Networks, 📝ICLR OpenReview, :octocat:Code
  • Comparing and Detecting Adversarial Attacks for Graph Deep Learning, 📝[email protected]
  • Virtual Adversarial Training on Graph Convolutional Networks in Node Classification, 📝PRCV
  • Robust Graph Convolutional Networks Against Adversarial Attacks, 📝KDD, :octocat:Code
  • Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications, 📝NAACL, :octocat:Code
  • Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective, 📝IJCAI, :octocat:Code

2018

💨 Back to Top

2017

💨 Back to Top

🔐Certification

💨 Back to Top

  • Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation, 📝KDD'2021, :octocat:Code
  • Collective Robustness Certificates, 📝ICLR'2021
  • Adversarial Immunization for Improving Certifiable Robustness on Graphs, 📝WSDM'2021
  • Certifying Robustness of Graph Laplacian Based Semi-Supervised Learning, 📝ICLR OpenReview'2021
  • Robust Certification for Laplace Learning on Geometric Graphs, 📝MSML’2021
  • Improving the Robustness of Wasserstein Embedding by Adversarial PAC-Bayesian Learning, 📝AAAI'2020
  • Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks, 📝NeurIPS'2020, :octocat:Code
  • Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing, 📝WWW'2020
  • Efficient Robustness Certificates for Discrete Data: Sparsity - Aware Randomized Smoothing for Graphs, Images and More, 📝ICML'2020, :octocat:Code
  • Abstract Interpretation based Robustness Certification for Graph Convolutional Networks, 📝ECAI'2020
  • Certifiable Robustness of Graph Convolutional Networks under Structure Perturbation, 📝KDD'2020, :octocat:Code
  • Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing, 📝NeurIPS'2020
  • Certifiable Robustness and Robust Training for Graph Convolutional Networks, 📝KDD'2019, :octocat:Code
  • Certifiable Robustness to Graph Perturbations, 📝NeurIPS'2019, :octocat:Code

⚖Stability

💨 Back to Top

🚀Others

💨 Back to Top

📃Survey

💨 Back to Top

⚙Toolbox

💨 Back to Top

🔗Resource

💨 Back to Top

  • Awesome Adversarial Learning on Recommender System :octocat:Link
  • Awesome Graph Attack and Defense Papers :octocat:Link
  • Graph Adversarial Learning Literature :octocat:Link
  • A Complete List of All (arXiv) Adversarial Example Papers 🌐Link
  • Adversarial Attacks and Defenses Frontiers, Advances and Practice, KDD'20 tutorial, 🌐Link

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.