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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:

Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
(2019), pp. 6246-6250
Open Access | Times Cited: 275

Showing 1-25 of 275 citing articles:

A Comprehensive Survey on Graph Neural Networks
Zonghan Wu, Shirui Pan, Fengwen Chen, et al.
IEEE Transactions on Neural Networks and Learning Systems (2020) Vol. 32, Iss. 1, pp. 4-24
Open Access | Times Cited: 3360
Explainable AI: A Review of Machine Learning Interpretability Methods
Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis
Entropy (2020) Vol. 23, Iss. 1, pp. 18-18
Open Access | Times Cited: 1861
Graph Structure Learning for Robust Graph Neural Networks
Wei Jin, Yao Ma, Xiaorui Liu, et al.
(2020)
Open Access | Times Cited: 452
Adversarial Examples for Graph Data: Deep Insights into Attack and Defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, et al.
(2019), pp. 4816-4823
Open Access | Times Cited: 319
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Kaidi Xu, Hongge Chen, Sijia Liu, et al.
(2019), pp. 3961-3967
Open Access | Times Cited: 275
A gentle introduction to deep learning for graphs
Davide Bacciu, Federico Errica, Alessio Micheli, et al.
Neural Networks (2020) Vol. 129, pp. 203-221
Open Access | Times Cited: 267
Graph-based deep learning for communication networks: A survey
Weiwei Jiang
Computer Communications (2021) Vol. 185, pp. 40-54
Open Access | Times Cited: 189
Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification
Yingxue Zhang, Soumyasundar Pal, Mark Coates, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2019) Vol. 33, Iss. 01, pp. 5829-5836
Open Access | Times Cited: 176
Backdoor Attacks to Graph Neural Networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, et al.
(2021)
Open Access | Times Cited: 149
Graph Structure Learning with Variational Information Bottleneck
Qingyun Sun, Jianxin Li, Hao Peng, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 4, pp. 4165-4174
Open Access | Times Cited: 88
Graph Neural Networks: Taxonomy, Advances, and Trends
Yu Zhou, Haixia Zheng, Xin Huang, et al.
ACM Transactions on Intelligent Systems and Technology (2022) Vol. 13, Iss. 1, pp. 1-54
Open Access | Times Cited: 80
Trustworthy Graph Neural Networks: Aspects, Methods, and Trends
He Zhang, Bang Ye Wu, Xingliang Yuan, et al.
Proceedings of the IEEE (2024) Vol. 112, Iss. 2, pp. 97-139
Open Access | Times Cited: 30
Attacking Graph-based Classification via Manipulating the Graph Structure
Binghui Wang, Neil Zhenqiang Gong
Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (2019), pp. 2023-2040
Open Access | Times Cited: 121
Certifiable Robustness and Robust Training for Graph Convolutional Networks
Daniel Zügner, Stephan Günnemann
(2019), pp. 246-256
Open Access | Times Cited: 115
Political communication on social media: A tale of hyperactive users and bias in recommender systems
Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich
Online Social Networks and Media (2019) Vol. 15, pp. 100058-100058
Open Access | Times Cited: 105
Using attribution to decode binding mechanism in neural network models for chemistry
Kevin McCloskey, Ankur Taly, Federico Monti, et al.
Proceedings of the National Academy of Sciences (2019) Vol. 116, Iss. 24, pp. 11624-11629
Open Access | Times Cited: 101
Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems
Giovanni Apruzzese, Mauro Andreolini, Luca Ferretti, et al.
Digital Threats Research and Practice (2021) Vol. 3, Iss. 3, pp. 1-19
Open Access | Times Cited: 93
Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks
Xianfeng Tang, Huaxiu Yao, Yiwei Sun, et al.
(2020)
Open Access | Times Cited: 87
Transferring Robustness for Graph Neural Network Against Poisoning Attacks
Xianfeng Tang, Yandong Li, Yiwei Sun, et al.
(2020)
Open Access | Times Cited: 81
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Kaize Ding, Qinghai Zhou, Hanghang Tong, et al.
(2021)
Open Access | Times Cited: 81
Adversarial Attack on Community Detection by Hiding Individuals
Jia Li, Honglei Zhang, Zhichao Han, et al.
(2020)
Open Access | Times Cited: 75
Graph convolutional networks for graphs containing missing features
Hibiki Taguchi, Xin Liu, Tsuyoshi Murata
Future Generation Computer Systems (2020) Vol. 117, pp. 155-168
Open Access | Times Cited: 72
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Daniel Schwalbe‐Koda, Aik Rui Tan, Rafael Gómez‐Bombarelli
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 71
Fake Node Attacks on Graph Convolutional Networks
Xiaoyun Wang, Minhao Cheng, Joe Eaton, et al.
Journal of Computational and Cognitive Engineering (2022) Vol. 1, Iss. 4, pp. 165-173
Open Access | Times Cited: 67
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