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Paper List of Adversarial Robustness in Graph Machine Learning

This list collects papers related to the adversarial robustness in graph machine learning.

  1. TDGIA: Effective Injection Attacks on Graph Neural Networks. Zou Xu, Zheng Qinkai, Dong Yuxiao, Guan Xinyu, Kharlamov Evgeny, Lu Jialiang, Tang Jie. KDD 2021.

  2. Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. Sun Yiwei, Wang Suhang, Tang Xianfeng, Hsieh Tsung-Yu, Honavar Vasant. WWW 2020.

  3. Graph Structure Learning for Robust Graph Neural Networks. Jin Wei, Ma Yao, Liu Xiaorui, Tang Xianfeng, Wang Suhang, Tang Jiliang. arXiv preprint arXiv:2005.10203 2020.

  4. All You Need Is Low (Rank) Defending Against Adversarial Attacks on Graphs. Entezari Negin, Al-Sayouri Saba A, Darvishzadeh Amirali, Papalexakis Evangelos E. WSDM 2020.

  5. Gnnguard: Defending graph neural networks against adversarial attacks. Zhang Xiang, Zitnik Marinka. arXiv preprint arXiv:2006.08149 2020.

  6. Graph Random Neural Networks for Semi-Supervised Learning on Graphs. Feng Wenzheng, Zhang Jie, Dong Yuxiao, Han Yu, Luan Huanbo, Xu Qian, Yang Qiang, Kharlamov Evgeny, Tang Jie. Advances in Neural Information Processing Systems 2020.

  7. KDD CUP 2020 ML Track 2 Adversarial Attacks and Defense on Academic Graph 1st Place Solution. Zheng Qinkai, Fei Yixiao, Li Yanhao, Liu Qingmin, Hu Minhao, Sun Qibo. 2020.

  8. Scalable Attack on Graph Data by Injecting Vicious Nodes. Wang Jihong, Luo Minnan, Suya Fnu, Li Jundong, Yang Zijiang, Zheng Qinghua. arXiv preprint arXiv:2004.13825 2020.

  9. Query-free Black-box Adversarial Attacks on Graphs. Xu Jiarong, Sun Yizhou, Jiang Xin, Wang Yanhao, Yang Yang, Wang Chunping, Lu Jiangang. arXiv preprint arXiv:2012.06757 2020.

  10. Simplifying graph convolutional networks. Wu Felix, Souza Amauri, Zhang Tianyi, Fifty Christopher, Yu Tao, Weinberger Kilian. ICML 2019.

  11. Adversarial attacks on graph neural networks via meta learning. Zügner Daniel, Günnemann Stephan. arXiv preprint arXiv:1902.08412 2019.

  12. Attacking graph convolutional networks via rewiring. Ma Yao, Wang Suhang, Derr Tyler, Wu Lingfei, Tang Jiliang. arXiv preprint arXiv:1906.03750 2019.

  13. Robust Graph Representation Learning via Neural Sparsification. Zheng Cheng, Zong Bo, Cheng Wei, Song Dongjin, Ni Jingchao, Yu Wenchao, Chen Haifeng, Wang Wei. 2019.

  14. Robust graph convolutional networks against adversarial attacks. Zhu Dingyuan, Zhang Ziwei, Cui Peng, Zhu Wenwu. KDD 2019.

  15. Adversarial examples on graph data: Deep insights into attack and defense. Wu Huijun, Wang Chen, Tyshetskiy Yuriy, Docherty Andrew, Lu Kai, Zhu Liming. arXiv preprint arXiv:1903.01610 2019.

  16. Dimensional Reweighting Graph Convolution Networks. Zou Xu, Jia Qiuye, Zhang Jianwei, Zhou Chang, Yao Zijun, Yang Hongxia, Tang Jie. 2019.

  17. Graph adversarial training: Dynamically regularizing based on graph structure. Feng Fuli, He Xiangnan, Tang Jie, Chua Tat-Seng. IEEE Transactions on Knowledge and Data Engineering 2019.

  18. Adversarial attacks on node embeddings via graph poisoning. Bojchevski Aleksandar, Günnemann Stephan. International Conference on Machine Learning 2019.

  19. How Powerful are Graph Neural Networks?. Xu Keyulu, Hu Weihua, Leskovec Jure, Jegelka Stefanie. ICLR 2018. paper code

  20. Graph Attention Networks. Veličković Petar, Cucurull Guillem, Casanova Arantxa, Romero Adriana, Liò Pietro, Bengio Yoshua. ICLR 2018.

  21. Graph convolutional neural networks for web-scale recommender systems. Ying Rex, He Ruining, Chen Kaifeng, Eksombatchai Pong, Hamilton William L, Leskovec Jure. KDD 2018.

  22. Adversarial attacks on neural networks for graph data. Zügner Daniel, Akbarnejad Amir, Günnemann Stephan. KDD 2018.

  23. Hiding individuals and communities in a social network. Waniek Marcin, Michalak Tomasz P, Wooldridge Michael J, Rahwan Talal. Nature Human Behaviour 2018.

  24. Adversarial attack on graph structured data. Dai Hanjun, Li Hui, Tian Tian, Huang Xin, Wang Lin, Zhu Jun, Song Le. International conference on machine learning 2018.

  25. Predict then propagate: Graph neural networks meet personalized pagerank. Klicpera Johannes, Bojchevski Aleksandar, Günnemann Stephan. arXiv preprint arXiv:1810.05997 2018.

  26. Fast gradient attack on network embedding. Chen Jinyin, Wu Yangyang, Xu Xuanheng, Chen Yixian, Zheng Haibin, Xuan Qi. arXiv preprint arXiv:1809.02797 2018.

  27. Inductive representation learning on large graphs. Hamilton Will, Ying Zhitao, Leskovec Jure. NeurIPS 2017.

  28. Topology adaptive graph convolutional networks. Du Jian, Zhang Shanghang, Wu Guanhang, Moura José MF, Kar Soummya. arXiv preprint arXiv:1710.10370 2017.

  29. Towards deep learning models resistant to adversarial attacks. Madry Aleksander, Makelov Aleksandar, Schmidt Ludwig, Tsipras Dimitris, Vladu Adrian. arXiv preprint arXiv:1706.06083 2017.

  30. Semi-supervised classification with graph convolutional networks. Kipf Thomas N, Welling Max. arXiv preprint arXiv:1609.02907 2016. paper code

  31. A review of relational machine learning for knowledge graphs. Nickel Maximilian, Murphy Kevin, Tresp Volker, Gabrilovich Evgeniy. Proceedings of the IEEE 2015.