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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:

GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
Qiang Huang, Makoto Yamada, Yuan Tian, et al.
IEEE Transactions on Knowledge and Data Engineering (2022) Vol. 35, Iss. 7, pp. 6968-6972
Open Access | Times Cited: 233

Showing 1-25 of 233 citing articles:

A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion
A. S. Albahri, Ali M. Duhaim, Mohammed A. Fadhel, et al.
Information Fusion (2023) Vol. 96, pp. 156-191
Closed Access | Times Cited: 352
Explainability in Graph Neural Networks: A Taxonomic Survey
Hao Yuan, Haiyang Yu, Shurui Gui, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022), pp. 1-19
Open Access | Times Cited: 314
Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI
Andreas Holzinger, Bernd Malle, Anna Saranti, et al.
Information Fusion (2021) Vol. 71, pp. 28-37
Open Access | Times Cited: 289
Survey of Explainable AI Techniques in Healthcare
Ahmad Chaddad, Jihao Peng, Jian Xu, et al.
Sensors (2023) Vol. 23, Iss. 2, pp. 634-634
Open Access | Times Cited: 269
Explainable AI Methods - A Brief Overview
Andreas Holzinger, Anna Saranti, Christoph Molnar, et al.
Lecture notes in computer science (2022), pp. 13-38
Open Access | Times Cited: 232
Machine Learning Methods for Small Data Challenges in Molecular Science
Bozheng Dou, Zailiang Zhu, Ekaterina Merkurjev, et al.
Chemical Reviews (2023) Vol. 123, Iss. 13, pp. 8736-8780
Open Access | Times Cited: 181
Graph Neural Networks in Network Neuroscience
Alaa Bessadok, Mohamed Ali Mahjoub, Islem Rekik
IEEE Transactions on Pattern Analysis and Machine Intelligence (2022) Vol. 45, Iss. 5, pp. 5833-5848
Open Access | Times Cited: 166
Interpretable machine learning with an ensemble of gradient boosting machines
Andrei V. Konstantinov, Lev V. Utkin
Knowledge-Based Systems (2021) Vol. 222, pp. 106993-106993
Open Access | Times Cited: 157
Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions
Nour Moustafa, Nickolaos Koroniotis, Marwa Keshk, et al.
IEEE Communications Surveys & Tutorials (2023) Vol. 25, Iss. 3, pp. 1775-1807
Closed Access | Times Cited: 84
Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking
Zhenhua Wu, Jike Wang, Hongyan Du, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 72
Evaluating explainability for graph neural networks
Chirag Agarwal, Owen Queen, Himabindu Lakkaraju, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 71
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
Ahmed Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, et al.
Advanced Intelligent Systems (2024)
Open Access | Times Cited: 58
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Ahmad Chaddad, Qizong Lu, Jiali Li, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 4, pp. 859-876
Open Access | Times Cited: 45
Graph neural networks
Gabriele Corso, H. Stärk, Stefanie Jegelka, et al.
Nature Reviews Methods Primers (2024) Vol. 4, Iss. 1
Closed Access | Times Cited: 43
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
Advancing material property prediction: using physics-informed machine learning models for viscosity
Alex K. Chew, Matthew Sender, Zachary Kaplan, et al.
Journal of Cheminformatics (2024) Vol. 16, Iss. 1
Open Access | Times Cited: 23
SurvLIME: A method for explaining machine learning survival models
Maxim S. Kovalev, Lev V. Utkin, Ernest M. Kasimov
Knowledge-Based Systems (2020) Vol. 203, pp. 106164-106164
Open Access | Times Cited: 87
Interpreting Deep Learning-Based Networking Systems
Zili Meng, Minhu Wang, Jiasong Bai, et al.
(2020)
Open Access | Times Cited: 84
A survey on graph-based deep learning for computational histopathology
David Ahmedt‐Aristizabal, Mohammad Ali Armin, Simon Denman, et al.
Computerized Medical Imaging and Graphics (2021) Vol. 95, pp. 102027-102027
Open Access | Times Cited: 77
Functions predict horizontal gene transfer and the emergence of antibiotic resistance
Hao Zhou, Juan Felipe Beltrán, Ilana Brito
Science Advances (2021) Vol. 7, Iss. 43
Open Access | Times Cited: 69
ProtGNN: Towards Self-Explaining Graph Neural Networks
Zaixi Zhang, Qi Liu, Hao Wang, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 8, pp. 9127-9135
Open Access | Times Cited: 69
Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia
Du Lei, Kun Qin, Walter Hugo Lopez Pinaya, et al.
Schizophrenia Bulletin (2022) Vol. 48, Iss. 4, pp. 881-892
Open Access | Times Cited: 45
Convolutional Neural Network
Xiu Zhang, Xin Zhang, Sheng Wang
(2023), pp. 39-71
Closed Access | Times Cited: 35
Advances of machine learning in materials science: Ideas and techniques
Sue Sin Chong, Yi Sheng Ng, Hui‐Qiong Wang, et al.
Frontiers of Physics (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 24
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Dongcheng Zou, Hao Peng, Xiang Huang, et al.
Proceedings of the ACM Web Conference 2022 (2023), pp. 499-510
Open Access | Times Cited: 23
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