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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:

Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, Wenwu Zhu
IEEE Transactions on Knowledge and Data Engineering (2020) Vol. 34, Iss. 1, pp. 249-270
Open Access | Times Cited: 1234

Showing 1-25 of 1234 citing articles:

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al.
Journal Of Big Data (2021) Vol. 8, Iss. 1
Open Access | Times Cited: 4801
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou, Ganqu Cui, Shengding Hu, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 1160
A survey on deep learning and its applications
Shi Dong, Ping Wang, Khushnood Abbas
Computer Science Review (2021) Vol. 40, pp. 100379-100379
Closed Access | Times Cited: 1057
Graph neural network for traffic forecasting: A survey
Weiwei Jiang, Jiayun Luo
Expert Systems with Applications (2022) Vol. 207, pp. 117921-117921
Open Access | Times Cited: 751
Study on artificial intelligence: The state of the art and future prospects
Caiming Zhang, Yang Lu
Journal of Industrial Information Integration (2021) Vol. 23, pp. 100224-100224
Closed Access | Times Cited: 730
Hypergraph convolution and hypergraph attention
Song Bai, Feihu Zhang, Philip H. S. Torr
Pattern Recognition (2020) Vol. 110, pp. 107637-107637
Open Access | Times Cited: 490
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions
Mohammad Mustafa Taye
Computers (2023) Vol. 12, Iss. 5, pp. 91-91
Open Access | Times Cited: 459
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
Xiao Wang, Meiqi Zhu, Deyu Bo, et al.
(2020), pp. 1243-1253
Open Access | Times Cited: 398
The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
Tianfu Li, Zheng Zhou, Sinan Li, et al.
Mechanical Systems and Signal Processing (2021) Vol. 168, pp. 108653-108653
Closed Access | Times Cited: 321
Contrastive Multi-View Representation Learning on Graphs
Kaveh Hassani, Amir Hosein Khasahmadi
arXiv (Cornell University) (2020)
Open Access | Times Cited: 293
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
Le Wu, Xiangnan He, Xiang Wang, et al.
IEEE Transactions on Knowledge and Data Engineering (2022), pp. 1-1
Open Access | Times Cited: 277
Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions
Mohammad Mustafa Taye
Computation (2023) Vol. 11, Iss. 3, pp. 52-52
Open Access | Times Cited: 273
Text Level Graph Neural Network for Text Classification
Lianzhe Huang, Dehong Ma, Sujian Li, et al.
(2019)
Open Access | Times Cited: 262
HyGCN: A GCN Accelerator with Hybrid Architecture
Mingyu Yan, Lei Deng, Xing Hu, et al.
(2020), pp. 15-29
Open Access | Times Cited: 259
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Xiao Wang, Deyu Bo, Chuan Shi, et al.
IEEE Transactions on Big Data (2022) Vol. 9, Iss. 2, pp. 415-436
Open Access | Times Cited: 232
A Survey on Evolutionary Constrained Multiobjective Optimization
Jing Liang, Xuanxuan Ban, Kunjie Yu, et al.
IEEE Transactions on Evolutionary Computation (2022) Vol. 27, Iss. 2, pp. 201-221
Open Access | Times Cited: 211
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
Joakim Skarding, Bogdan Gabryś, Katarzyna Musiał
IEEE Access (2021) Vol. 9, pp. 79143-79168
Open Access | Times Cited: 209
Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 9, pp. 1-38
Open Access | Times Cited: 199
A Review of Graph Neural Networks and Their Applications in Power Systems
Wenlong Liao, Birgitte Bak‐Jensen, Jayakrishnan Radhakrishna Pillai, et al.
Journal of Modern Power Systems and Clean Energy (2022) Vol. 10, Iss. 2, pp. 345-360
Open Access | Times Cited: 196
Graph-based deep learning for communication networks: A survey
Weiwei Jiang
Computer Communications (2021) Vol. 185, pp. 40-54
Open Access | Times Cited: 189
Utilizing graph machine learning within drug discovery and development
Thomas Gaudelet, Ben Day, Arian R. Jamasb, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Open Access | Times Cited: 187
Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence
Uzair Aslam Bhatti, Hao Tang, Guilu Wu, et al.
International Journal of Intelligent Systems (2023) Vol. 2023, pp. 1-28
Open Access | Times Cited: 185
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Fuli Feng, Xiangnan He, Jie Tang, et al.
IEEE Transactions on Knowledge and Data Engineering (2019) Vol. 33, Iss. 6, pp. 2493-2504
Closed Access | Times Cited: 177
Medical deep learning—A systematic meta-review
Jan Egger, Christina Gsaxner, Antonio Pepe, et al.
Computer Methods and Programs in Biomedicine (2022) Vol. 221, pp. 106874-106874
Open Access | Times Cited: 173
Graph Neural Networks for Anomaly Detection in Industrial Internet of Things
Yulei Wu, Hong‐Ning Dai, Haina Tang
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 12, pp. 9214-9231
Open Access | Times Cited: 171
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