Fetching the paper…
Reading the bibliography…
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
Graph convolutional networks using heat kernel for semi-supervised learning. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19) . 1928–1934
Bingbing Xu, Huawei Shen, Qi Cao, Keting Cen, and Xueqi Cheng. 2019b · 1934
Earlier work this paper cites.
The PageRank Citation Ranking: Bringing Order to the Web
Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999 · 1999
Earlier work this paper cites.
Community structure in social and biological networks
Michelle Girvan and Mark EJ Newman. 2002 · 2002
Earlier work this paper cites.
Towards Evaluating the Robustness of Neural Networks. In 2017 IEEE Symposium on Security and Privacy (SP ’17) . 39–57
Nicholas Carlini and David A. Wagner. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In Proceedings of the 34th International Conference on Machine Learning (ICML ’17) . 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Inductive Representation Learning on Large Graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS’17) . 1025–1035
William L. Hamilton, Rex Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR ’17)
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
Struc2vec: Learning Node Representations from Structural Identity. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’17) . 385–394
Leonardo F.R. Ribeiro, Pedro H.P. Saverese, and Daniel R. Figueiredo. 2017 · 2017
Earlier work this paper cites.
Adversarial Attack on Graph Structured Data. In Proceedings of the 35th International Conference on Machine Learning (ICML ’18) . 1123–1132
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018 · 2018
Earlier work this paper cites.
Adaptive Sampling Towards Fast Graph Representation Learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018 · 2018
Earlier work this paper cites.
DeepInf: Social Influence Prediction with Deep Learning. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18) . 2110–2119
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018 · 2018
Earlier work this paper cites.
Adversarial Attack and Defense on Graph Data: A Survey
Lichao Sun, Ji Wang, Philip S. Yu, and Bo Li. 2018 · 2018
Earlier work this paper cites.
Graph Attention Networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR ’18)
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018 · 2018
Earlier work this paper cites.
Bayesian graph convolutional neural networks for semi-supervised classification. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI ’18) . 5829–5836
Yingxue Zhang, Soumyasundar Pal, Mark Coates, and Deniz Üstebay. 2018 · 2018
Earlier work this paper cites.
Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18) . 2847–2856
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. 2018 · 2018
Cited alongside, same era.
Adversarial Attacks on Node Embeddings via Graph Poisoning. In Proceedings of the 36th International Conference on Machine Learning (ICML ’19) . 695–704
Aleksandar Bojchevski and Stephan Günnemann. 2019 · 2019
Cited alongside, same era.
Certifiable Robustness to Graph Perturbations
Aleksandar Bojchevski and Stephan Günnemann. 2019 · 2019
Cited alongside, same era.
Adversarial Training Methods for Network Embedding. In Proceedings of the World Wide Web Conference (WWW ’19) . 329–339
Quanyu Dai, Xiao Shen, Liang Zhang, Qiang Li, and Dan Wang. 2019 · 2019
Cited alongside, same era.
Graph Neural Networks for Social Recommendation. In Proceedings of the World Wide Web Conference (WWW ’19) . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More. In Proceedings of the 37th International Conference on Machine Learning (ICML ’20) . 11647–11657
Aleksandar Bojchevski, Johannes Klicpera, and Stephan Günnemann. 2020 · 2020
Closest in time.
Popularity Prediction on Social Platforms with Coupled Graph Neural Networks. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM ’20) . 70–78
Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, and Xueqi Cheng. 2020 · 2020
Closest in time.
A Survey of Adversarial Learning on Graphs
Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng. 2020 · 2020
Closest in time.
All You Need Is Low (Rank): Defending Against Adversarial Attacks on Graphs. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM ’20) . 169–177
Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis. 2020 · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Graph adversarial training: Dynamically regularizing based on graph structure
Fuli Feng, Xiangnan He, Jie Tang, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In Proceedings of the 7th International Conference on Learning Representations (ICLR ’19)
Johannes Klicpera, Aleksandar Bojchevski, and Stephan Günnemann. 2019 · 2019
Cited alongside, same era.
A persistent weisfeiler-lehman procedure for graph classification. In Proceedings of the 36th International Conference on Machine Learning (ICML ’19) . 5448–5458
Bastian Rieck, Christian Bock, and Karsten Borgwardt. 2019 · 2019
Cited alongside, same era.
Adversarial Examples for Graph Data: Deep Insights into Attack and Defense. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19) . 4816–4823
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019 · 2019
Cited alongside, same era.
Comparing and detecting adversarial attacks for graph deep learning. In Proc. Representation Learning on Graphs and Manifolds Workshop, Int. Conf. Learning Representations, New Orleans, LA, USA (RLGM @ ICLR ’19)
Yingxue Zhang, S Khan, and Mark Coates. 2019 · 2019
Cited alongside, same era.
Robust Graph Convolutional Networks Against Adversarial Attacks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19) . 1399–1407
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2019 · 2019
Cited alongside, same era.
Adversarial Attacks on Graph Neural Networks via Meta Learning. In Proceedings of the 7th International Conference on Learning Representations (ICLR ’19)
Daniel Zügner and Stephan Günnemann. 2019 · 2019
Cited alongside, same era.
Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing. In Proceedings of The Web Conference 2020 (WWW ’20) . 2718–2724
Jinyuan Jia, Binghui Wang, Xiaoyu Cao, and Neil Zhenqiang Gong. 2020 · 2020
Closest in time.
Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang. 2020b · 2020
Closest in time.
Certifiable Robustness to Discrete Adversarial Perturbations for Factorization Machines. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20) . 419–428
Yang Liu, Xianzhuo Xia, Liang Chen, X. He, Carl Yang, and Z. Zheng. 2020 · 2020
Closest in time.
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. In Proceedings of The Web Conference 2020 (WWW ’20) . 673–683
Yiwei Sun, Suhang Wang, Xian-Feng Tang, Tsung-Yu Hsieh, and Vasant G Honavar. 2020 · 2020
Closest in time.
Transferring Robustness for Graph Neural Network Against Poisoning Attacks. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM ’20) . 600–608
Xian-Feng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang. 2020 · 2020
Closest in time.
Scalable Attack on Graph Data by Injecting Vicious Nodes
Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, and Qinghua Zheng. 2020 · 2020
Closest in time.
GraphSAINT: Graph Sampling Based Inductive Learning Method. In Proceedings of the 8th International Conference on Learning Representations (ICLR ’20)
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020 · 2020
Closest in time.
Daniel Zügner and Stephan Günnemann. 2020 · 2020
Closest in time.
Attacking Graph-Based Classification via Manipulating the Graph Structure. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS ’19) . 2023–2040
Binghui Wang and Neil Zhenqiang Gong. 2019 · 2040
Closest in time.