This repository aims to provide links to works about adversarial attacks and defenses on graph data or GNN (Graph Neural Networks).

• 1. Survey Papers
• 2. Attack Papers (classified according to attack goal)
  • 2.1 Targeted Attack
  • 2.2 Untargeted Attack
• 3. Defense Papers
• 4. Certified Robustness Papers

1. Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study. Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, Jiliang Tang. arxiv, 2020. [paper] [code]
2. Adversarial Attacks and Defenses in Images, Graphs and Text: A Review. Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain. arxiv, 2019. [paper]
3. Adversarial Attack and Defense on Graph Data: A Survey. Lichao Sun, Ji Wang, Philip S. Yu, Bo Li. arviv 2018. [paper]

1. MGA: Momentum Gradient Attack on Network. Jinyin Chen, Yixian Chen, Haibin Zheng, Shijing Shen, Shanqing Yu, Dan Zhang, Qi Xuan. [[paper]](Jinyin Chen, Yixian Chen, Haibin Zheng, Shijing Shen, Shanqing Yu, Dan Zhang, Qi Xuan)
2. Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models. Xiao Zang, Yi Xie, Jie Chen, Bo Yuan. arxiv, 2020. [paper]
3. Time-aware Gradient Attack on Dynamic Network Link Prediction. Jinyin Chen, Jian Zhang, Zhi Chen, Min Du, Feifei Li, Qi Xuan. arxiv 2019. [paper]
4. Multiscale Evolutionary Perturbation Attack on Community Detection. Jinyin Chen, Yixian Chen, Lihong Chen, Minghao Zhao, and Qi Xuan. arxiv 2019. [paper]
5. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu. IJCAI 2019. [paper] [code]
6. Data Poisoning Attack against Knowledge Graph Embedding. Hengtong Zhang, Tianhang Zheng, Jing Gao, Chenglin Miao, Lu Su, Yaliang Li, Kui Ren. IJCAI 2019. [paper]
7. Attacking Graph-based Classification via Manipulating the Graph Structure. Binghui Wang, Neil Zhenqiang Gong. CCS 2019. [paper]
8. A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models. Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, Junzhou Huang. AAAI 2020. [paper] [code]
9. Adversarial Attacks on Node Embeddings via Graph Poisoning. Aleksandar Bojchevski, Stephan Günnemann. ICML 2019. [paper] [code]
10. Adversarial Attack on Graph Structured Data. Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, Le Song. ICML 2018. [paper] [code]
11. Fast Gradient Attack on Network Embedding. Jinyin Chen, Yangyang Wu, Xuanheng Xu, Yixian Chen, Haibin Zheng, Qi Xuan. arxiv 2018. [paper] [code]
12. Adversarial Attacks on Neural Networks for Graph Data. Daniel Zügner, Amir Akbarnejad, Stephan Günnemann. KDD 2018. [paper] [code]

1. Node Injection Attacks on Graphs via Reinforcement Learning. Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, Vasant Honavar. WWW 2020. [paper]
2. A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning. Xuanqing Liu, Si Si, Xiaojin(Jerry) Zhu, Yang Li, Cho-Jui Hsieh. NeurIPS 2019. [paper]
3. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu. IJCAI 2019. [paper] [code]
4. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin. ICJAI 2019. [paper] [code]
5. Adversarial Attacks on Node Embeddings via Graph Poisoning. Aleksandar Bojchevski, Stephan Günnemann. ICML 2019. [paper] [code]
6. Adversarial Attacks on Graph Neural Networks via Meta Learning. Daniel Zugner, Stephan Gunnemann. ICLR 2019. [paper] [code]
7. Attacking Graph Convolutional Networks via Rewiring. Yao Ma, Suhang Wang, Lingfei Wu, Jiliang Tang. arxiv 2019. [paper]

1. How Robust Are Graph Neural Networks to Structural Noise? James Fox, Sivasankaran Rajamanickam. arxiv 2020. [paper]
2. GraphDefense: Towards Robust Graph Convolutional Networks. Xiaoyun Wang, Xuanqing Liu, Cho-Jui Hsieh. arxiv 2019. [paper]
3. All You Need is Low (Rank): Defending Against Adversarial Attacks on Graphs. Negin Entezari, Saba Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis. WSDM 2020. [paper] [code]
4. Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua. TKDE 2019. [paper]
5. Edge Dithering for Robust Adaptive Graph Convolutional Networks. Vassilis N. Ioannidis, Georgios B. Giannakis. arxiv 2019. [paper]
6. GraphSAC: Detecting anomalies in large-scale graphs. Vassilis N. Ioannidis, Dimitris Berberidis, Georgios B. Giannakis. arxiv 2019. [paper]
7. Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning. Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, Suhang Wang. WSDM 2020. [paper]
8. Robust Graph Convolutional Networks Against Adversarial Attacks. Dingyuan Zhu, Ziwei Zhang, Peng Cui, Wenwu Zhu. KDD 2019. [paper]
9. Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu. IJCAI 2019. [paper] [code]
10. Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective. Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin. ICJAI 2019. [paper] [code]
11. Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering. Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi. arxiv 2019. [paper]
12. Latent Adversarial Training of Graph Convolution Networks. Hongwei Jin, Xinhua Zhang. ICML 2019 workshop. [paper]
13. Batch Virtual Adversarial Training for Graph Convolutional Networks. Zhijie Deng, Yinpeng Dong, Jun Zhu. ICML 2019 Workshop. [paper]
14. Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua. arXiv, 2019. [paper]

1. Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing. Jinyuan Jia, Binghui Wang, Xiaoyu Cao, Neil Zhenqiang Gong. WWW 2020. [paper]
2. Certifiable Robustness to Graph Perturbations. Aleksandar Bojchevski, Stephan Günnemann. NeurIPS 2019. [paper][code]
3. Certifiable Robustness and Robust Training for Graph Convolutional Networks. Daniel Zügner Stephan Günnemann. KDD 2019. [paper] [code]