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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:

Towards Self-Explainable Graph Neural Network
Enyan Dai, Suhang Wang
(2021), pp. 302-311
Open Access | Times Cited: 47

Showing 1-25 of 47 citing articles:

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: 29
Graph neural networks for financial fraud detection: a review
Dawei Cheng, Yao Zou, Sheng Xiang, et al.
Frontiers of Computer Science (2025) Vol. 19, Iss. 9
Open Access | Times Cited: 2
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels
Enyan Dai, Wei Jin, Hui Liu, et al.
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (2022), pp. 181-191
Open Access | Times Cited: 55
Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends
Haohui Lu, Shahadat Uddin
Healthcare (2023) Vol. 11, Iss. 7, pp. 1031-1031
Open Access | Times Cited: 30
Unnoticeable Backdoor Attacks on Graph Neural Networks
Enyan Dai, Minhua Lin, X. D. Zhang, et al.
Proceedings of the ACM Web Conference 2022 (2023), pp. 2263-2273
Open Access | Times Cited: 23
A Review of Graph Neural Networks in Epidemic Modeling
Zewen Liu, Guancheng Wan, B. Aditya Prakash, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2024), pp. 6577-6587
Open Access | Times Cited: 8
Counterfactual Learning on Graphs: A Survey
Zhimeng Guo, Zongyu Wu, Teng Xiao, et al.
Deleted Journal (2025) Vol. 22, Iss. 1, pp. 17-59
Open Access | Times Cited: 1
Explainable Artificial Intelligence: An Updated Perspective
Agneza Krajna, Mihael Kovač, Mario Brčić, et al.
2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO) (2022), pp. 859-864
Closed Access | Times Cited: 23
Jointly Attacking Graph Neural Network and its Explanations
Wenqi Fan, Han Xu, Wei Jin, et al.
2022 IEEE 38th International Conference on Data Engineering (ICDE) (2023), pp. 654-667
Open Access | Times Cited: 14
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, et al.
Deleted Journal (2024)
Open Access | Times Cited: 5
Interpretable Graph Neural Networks for Heterogeneous Tabular Data
Amr Alkhatib, Henrik Boström
Lecture notes in computer science (2025), pp. 310-324
Closed Access
IMPO: Interpretable Memory-based Prototypical Pooling
Alessio Ragno, Roberto Capobianco
(2025), pp. 625-632
Closed Access
DyExplainer: Self-explainable Dynamic Graph Neural Network with Sparse Attentions
Tianchun Wang, Dongsheng Luo, Wei Cheng, et al.
ACM Transactions on Knowledge Discovery from Data (2025)
Closed Access
Causal invariance guides interpretable graph contrastive learning in fMRI analysis
Boyang Wei, Weiming Zeng, Yuhu Shi, et al.
Alexandria Engineering Journal (2025) Vol. 117, pp. 635-647
Closed Access
Graph-based Prototype Inverse-Projection for Identifying Cortical Sulcal Pattern Abnormalities in Congenital Heart Disease
Hyeokjin Kwon, Seungyeon Son, Sarah U. Morton, et al.
Medical Image Analysis (2025) Vol. 102, pp. 103538-103538
Closed Access
GraphXAI: a survey of graph neural networks (GNNs) for explainable AI (XAI)
Mauparna Nandan, Soma Mitra, Debashis De
Neural Computing and Applications (2025)
Closed Access
3DGraphX: Explaining 3D Molecular Graph Models via Incorporating Chemical Priors
Xufeng Liu, Dongsheng Luo, Wenhan Gao, et al.
(2025), pp. 859-870
Closed Access
Stealing Training Graphs from Graph Neural Networks
Minhua Lin, Enyan Dai, Junjie Xu, et al.
(2025), pp. 777-788
Closed Access
Explainable Graph-based Fraud Detection via Neural Meta-graph Search
Zidi Qin, Yang Liu, Qing He, et al.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (2022), pp. 4414-4418
Open Access | Times Cited: 14
Towards Faithful and Consistent Explanations for Graph Neural Networks
Tianxiang Zhao, Dongsheng Luo, X. D. Zhang, et al.
(2023), pp. 634-642
Open Access | Times Cited: 8
Invariant Node Representation Learning under Distribution Shifts with Multiple Latent Environments
Haoyang Li, Ziwei Zhang, Xin Wang, et al.
ACM transactions on office information systems (2023) Vol. 42, Iss. 1, pp. 1-30
Open Access | Times Cited: 7
A comprehensive and reliable feature attribution method: Double-sided remove and reconstruct (DoRaR)
Dong Chen Qin, George T. Amariucai, Daji Qiao, et al.
Neural Networks (2024) Vol. 173, pp. 106166-106166
Open Access | Times Cited: 2
Disambiguated Node Classification with Graph Neural Networks
Tianxiang Zhao, X. D. Zhang, Suhang Wang
Proceedings of the ACM Web Conference 2022 (2024), pp. 914-923
Open Access | Times Cited: 2
A survey of dynamic graph neural networks
Yanping Zheng, Yi Lü, Zhewei Wei
Frontiers of Computer Science (2024) Vol. 19, Iss. 6
Open Access | Times Cited: 2
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy
Enyan Dai, Limeng Cui, Zhengyang Wang, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 368-379
Open Access | Times Cited: 6
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