A curated list of adversarial attacks and defenses papers on graph-structured data.
Papers are sorted by their uploaded dates in descending order.
| Year | Title | Type | Target Task | Target Model | Venue | Link |
|---|---|---|---|---|---|---|
| 2019 | A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models | Attack | Node Classification | GCN, SGC | AAAI 2020 | Link |
| 2019 | Time-aware Gradient Attack on Dynamic Network Link Prediction | Attack | Link Prediction | Dynamic Network Embedding Algs | Arxiv | Link |
| 2019 | Multiscale Evolutionary Perturbation Attack on Community Detection | Attack | Community Detection | Community Metrics | Arxiv | Link |
| 2019 | A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning | Attack | Regression, Classification | Label Propagation, Manifold Regularization | NeurIPS 2019 | Link |
| 2019 | Attacking Graph Convolutional Networks via Rewiring | Attack | Node Classification | GCN | Openreview | Link |
| 2019 | Node Injection Attacks on Graphs via Reinforcement Learning | Attack | Node Classification | GCN | Arxiv | Link |
| 2019 | Unsupervised Euclidean Distance Attack on Network Embedding | Attack | Node Embedding | GCN | Arxiv | Link |
| 2019 | Generalizable Adversarial Attacks Using Generative Models | Attack | Node Classification | GCN | Arxiv | Link |
| 2019 | Vertex Nomination, Consistent Estimation, and Adversarial Modification | Attack | Vertex Nomination | VN Scheme | Arxiv | Link |
| 2019 | Towards Data Poisoning Attack against Knowledge Graph Embedding | Attack | Fact Plausibility Prediction | TransE, TransR | IJCAI 2019 | Link |
| 2018 | Adversarial Attacks on Node Embeddings via Graph Poisoning | Attack | Node Classification, Community Detection | node2vec, DeepWalk, GCN, LINE | ICML 2019 | Link |
| 2019 | Attacking Graph-based Classification via Manipulating the Graph Structure | Attack | Node Classification | Belief Propagation, GCN | CCS 2019 | Link |
| 2019 | Adversarial Attacks on Graph Neural Networks via Meta Learning | Attack | Node Classification | GCN, CLN, DeepWalk | ICLR 2019 | Link |
| 2018 | GA Based Q-Attack on Community Detection | Attack | Community Detection | Modularity, Community Detection Alg | IEEE TCSS | Link |
| 2018 | Data Poisoning Attack against Unsupervised Node Embedding Methods | Attack | Link Prediction | LINE, DeepWalk | Arxiv | Link |
| 2018 | Attack Graph Convolutional Networks by Adding Fake Nodes | Attack | Node Classification | GCN | Arxiv | Link |
| 2018 | Link Prediction Adversarial Attack | Attack | Link Prediction | GAE, GCN | Arxiv | Link |
| 2018 | Attack Tolerance of Link Prediction Algorithms: How to Hide Your Relations in a Social Network | Attack | Link Prediction | Traditional Link Prediction Algs | Scientific Reports | Link |
| 2018 | Attacking Similarity-Based Link Prediction in Social Networks | Attack | Link Prediction | local&global similarity metrics | AAMAS 2019 | Link |
| 2018 | Fast Gradient Attack on Network Embedding | Attack | Node Classification | GCN | Arxiv | Link |
| 2018 | Adversarial Attack on Graph Structured Data | Attack | Node/Graph Classification | GNN, GCN | ICML 2018 | Link |
| 2018 | Adversarial Attacks on Neural Networks for Graph Data | Attack | Node Classification | GCN | KDD 2018 | Link |
| 2017 | Practical Attacks Against Graph-based Clustering | Attack | Graph Clustering | SVD, node2vec, Community Detection Alg | CCS 2017 | Link |
| 2017 | Adversarial Sets for Regularising Neural Link Predictors | Attack | Link Prediction | Knowledge Graph Embeddings | UAI 2017 | Link |
| Year | Title | Type | Target Task | Target Model | Venue | Link |
|---|---|---|---|---|---|---|
| 2019 | GraphDefense: Towards Robust Graph Convolutional Networks | Defense | Node Classification | GCN | Arxiv | Link |
| 2019 | All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs | Defense | Node Classification | GCN, Tensor Embedding | WSDM 2020 | Link |
| 2019 | αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model | Defense | Malware Detection | HIN | CIKM 2019 | Link |
| 2019 | Edge Dithering for Robust Adaptive Graph Convolutional Networks | Defense | Node Classification | GCN | Arxiv | Link |
| 2019 | GraphSAC: Detecting anomalies in large-scale graphs | Defense | Anomaly Detection | Anomaly Detection Algs | Arxiv | Link |
| 2019 | Certifiable Robustness to Graph Perturbations | Defense | Node Classification | GNN | NeurIPS 2019 | Link |
| 2019 | Power up! Robust Graph Convolutional Network based on Graph Powering | Defense | Node Classification | GCN | Openreview | Link |
| 2019 | Adversarial Robustness of Similarity-Based Link Prediction | Defense | Link Prediction | local similarity metrics | ICDM 2019 | Link |
| 2019 | Transferring Robustness for Graph Neural Network Against Poisoning Attacks | Defense | Node Classification | GNN | WSDM 2020 | Link |
| 2019 | Improving Robustness to Attacks Against Vertex Classification | Defense | Node Classification | GCN | KDD Workshop 2019 | Link |
| 2019 | Certifiable Robustness and Robust Training for Graph Convolutional Networks | Defense | Node Classification | GCN | KDD 2019 | Link |
| 2019 | Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective | Defense | Node Classification | GNN | IJCAI 2019 | Link |
| 2019 | Adversarial Examples on Graph Data: Deep Insights into Attack and Defense | Defense | Node Classification | GCN | IJCAI 2019 | Link |
| 2019 | Adversarial Defense Framework for Graph Neural Network | Defense | Node Classification | GCN, GraphSAGE | Arxiv | Link |
| 2019 | Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications | Defense | Link Prediction | Knowledge Graph Embedding | NAACL 2019 | Link |
| 2019 | Robust Graph Convolutional Networks Against Adversarial Attacks | Defense | Node Classification | GCN | KDD 2019 | Link |
| 2019 | Can Adversarial Network Attack be Defended? | Defense | Node Classification | GNN | Arxiv | Link |
| 2019 | Virtual Adversarial Training on Graph Convolutional Networks in Node Classification | Defense | Node Classification | GCN | PRCV | Link |
| 2019 | Comparing and Detecting Adversarial Attacks for Graph Deep Learning | Defense | Node Classification | GCN, GAT, Nettack | RLGM@ICLR 2019 | Link |
| 2019 | Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure | Defense | Node Classification | GCN | Arxiv | Link |
| 2018 | Characterizing Malicious Edges targeting on Graph Neural Networks | Defense | Detected Added Edges | GNN, GCN | OpenReview | Link |
| 2017 | Adversarial Sets for Regularising Neural Link Predictors | Attack | Link Prediction | Knowledge Graph Embeddings | UAI 2017 | Link |
Adversarial Attack and Defense on Graph Data: A Survey (Link)
@article{sun2018adversarial,
title={Adversarial Attack and Defense on Graph Data: A Survey},
author={Sun, Lichao and Wang, Ji and Yu, Philip S and Li, Bo},
journal={arXiv preprint arXiv:1812.10528},
year={2018}
}