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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Xianfeng Tang, Yandong Li, Yiwei Sun, et al.
(2020)
Open Access | Times Cited: 81

Showing 1-25 of 81 citing articles:

Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information
Enyan Dai, Suhang Wang
(2021), pp. 680-688
Open Access | Times Cited: 167
Intelligent financial fraud detection practices in post-pandemic era
Xiaoqian Zhu, Xiang Ao, Zidi Qin, et al.
The Innovation (2021) Vol. 2, Iss. 4, pp. 100176-100176
Open Access | Times Cited: 120
Adversarial Attacks and Defenses on Graphs
Wei Jin, Yaxing Li, Han Xu, et al.
ACM SIGKDD Explorations Newsletter (2021) Vol. 22, Iss. 2, pp. 19-34
Closed Access | Times Cited: 111
Transfer Graph Neural Networks for Pandemic Forecasting
George Panagopoulos, Giannis Nikolentzos, Michalis Vazirgiannis
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 6, pp. 4838-4845
Open Access | Times Cited: 103
GNNGuard: Defending Graph Neural Networks against Adversarial Attacks
Xiang Zhang, Marinka Žitnik
arXiv (Cornell University) (2020)
Open Access | Times Cited: 99
Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, et al.
(2020)
Open Access | Times Cited: 87
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Kaize Ding, Qinghai Zhou, Hanghang Tong, et al.
(2021)
Open Access | Times Cited: 81
THREATRACE: Detecting and Tracing Host-Based Threats in Node Level Through Provenance Graph Learning
Su Wang, Zhiliang Wang, Tao Zhou, et al.
IEEE Transactions on Information Forensics and Security (2022) Vol. 17, pp. 3972-3987
Open Access | Times Cited: 45
Survey of Graph Neural Networks and Applications
Fan Liang, Cheng Qian, Wei Yu, et al.
Wireless Communications and Mobile Computing (2022) Vol. 2022, pp. 1-18
Open Access | Times Cited: 38
Deep Learning and Reinforcement Learning

Artificial intelligence (2023)
Open Access | Times Cited: 28
Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study
Wei Jin, Yaxin Li, Han Xu, et al.
(2020)
Closed Access | Times Cited: 59
A Survey of Adversarial Learning on Graphs
Liang Chen, Jintang Li, Jiaying Peng, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 55
NRGNN
Enyan Dai, Charų C. Aggarwal, Suhang Wang
(2021), pp. 227-236
Closed Access | Times Cited: 54
Towards Self-Explainable Graph Neural Network
Enyan Dai, Suhang Wang
(2021), pp. 302-311
Open Access | Times Cited: 47
Explainable Multivariate Time Series Classification
Tsung-Yu Hsieh, Suhang Wang, Yiwei Sun, et al.
(2021), pp. 607-615
Closed Access | Times Cited: 42
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Ye Wu, Xingliang Yuan, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 35
DECOR: Degree-Corrected Social Graph Refinement for Fake News Detection
Jiaying Wu, Bryan Hooi
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 2582-2593
Open Access | Times Cited: 18
Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation
Binghui Wang, Jinyuan Jia, Xiaoyu Cao, et al.
(2021), pp. 1645-1653
Open Access | Times Cited: 34
Understanding Structural Vulnerability in Graph Convolutional Networks
Liang Chen, Jintang Li, Qibiao Peng, et al.
(2021), pp. 2249-2255
Open Access | Times Cited: 33
Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
Yingwei Li, Song Bai, Cihang Xie, et al.
Lecture notes in computer science (2020), pp. 795-813
Open Access | Times Cited: 36
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
Wei Jin, Yaxin Li, Han Xu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 36
A Simple and Yet Fairly Effective Defense for Graph Neural Networks
Sofiane Ennadir, Yassine Abbahaddou, Johannes F. Lutzeyer, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 19, pp. 21063-21071
Open Access | Times Cited: 4
A Study of Defensive Methods to Protect Visual Recommendation Against Adversarial Manipulation of Images
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, et al.
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2021)
Closed Access | Times Cited: 24
Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook
Eren Kurshan, Hongda Shen, Haojie Yu
(2020), pp. 125-130
Closed Access | Times Cited: 25
Robustness of deep learning models on graphs: A survey
Jiarong Xu, Junru Chen, Siqi You, et al.
AI Open (2021) Vol. 2, pp. 69-78
Open Access | Times Cited: 23
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