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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
arXiv (Cornell University) (2018)
Open Access | Times Cited: 177

Showing 1-25 of 177 citing articles:

Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, et al.
AI Open (2020) Vol. 1, pp. 57-81
Open Access | Times Cited: 3779
GNNExplainer: Generating Explanations for Graph Neural Networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 601
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Xiaofei Wang, Yiwen Han, Victor C. M. Leung, et al.
IEEE Communications Surveys & Tutorials (2020) Vol. 22, Iss. 2, pp. 869-904
Open Access | Times Cited: 423
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
Graph convolutional networks for computational drug development and discovery
Mengying Sun, Sendong Zhao, Coryandar Gilvary, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 919-935
Open Access | Times Cited: 330
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: 268
HyGCN: A GCN Accelerator with Hybrid Architecture
Mingyu Yan, Lei Deng, Xing Hu, et al.
(2020), pp. 15-29
Open Access | Times Cited: 259
Data Augmentation for Graph Neural Networks
Tong Zhao, Yozen Liu, Leonardo Neves, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 12, pp. 11015-11023
Open Access | Times Cited: 250
Heterogeneous Graph Structure Learning for Graph Neural Networks
Jianan Zhao, Xiao Wang, Chuan Shi, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 5, pp. 4697-4705
Open Access | Times Cited: 214
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
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
Application of deep learning methods in biological networks
Shuting Jin, Xiangxiang Zeng, Feng Xia, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 2, pp. 1902-1917
Closed Access | Times Cited: 170
SIGN: Scalable Inception Graph Neural Networks
Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 164
Label Efficient Semi-Supervised Learning via Graph Filtering
Qimai Li, Xiao-Ming Wu, Han Liu, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 9574-9583
Open Access | Times Cited: 154
Music Recommendation via Hypergraph Embedding
Valerio La Gatta, Vincenzo Moscato, Mirko Pennone, et al.
IEEE Transactions on Neural Networks and Learning Systems (2022) Vol. 34, Iss. 10, pp. 7887-7899
Closed Access | Times Cited: 78
Constructing Neural Network Based Models for Simulating Dynamical Systems
Christian Møldrup Legaard, Thomas Schranz, Gerald Schweiger, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 11, pp. 1-34
Open Access | Times Cited: 73
Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks
Amol Kapoor, Xue Ben, Luyang Liu, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 136
AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes
Huiyan Jiang, Tianyu Shi, Zhiqi Bai, et al.
IEEE Access (2019) Vol. 7, pp. 24898-24909
Open Access | Times Cited: 135
A Comprehensive Survey on Geometric Deep Learning
Wenming Cao, Zhiyue Yan, Zhiquan He, et al.
IEEE Access (2020) Vol. 8, pp. 35929-35949
Open Access | Times Cited: 113
Hierarchical Graph Pooling with Structure Learning
Zhen Zhang, Jiajun Bu, Martin Ester, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 110
Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
Luís C. Lamb, Artur S. d’Avila Garcez, Marco Gori, et al.
(2020), pp. 4877-4884
Open Access | Times Cited: 98
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, et al.
PeerJ Computer Science (2021) Vol. 7, pp. e357-e357
Open Access | Times Cited: 89
One-class graph neural networks for anomaly detection in attributed networks
Xuhong Wang, Baihong Jin, Ying Du, et al.
Neural Computing and Applications (2021) Vol. 33, Iss. 18, pp. 12073-12085
Closed Access | Times Cited: 87
Bilinear Graph Neural Network with Neighbor Interactions
Hongmin Zhu, Fuli Feng, Xiangnan He, et al.
(2020), pp. 1452-1458
Open Access | Times Cited: 86
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey
Xin Liu, Mingyu Yan, Lei Deng, et al.
IEEE/CAA Journal of Automatica Sinica (2021) Vol. 9, Iss. 2, pp. 205-234
Open Access | Times Cited: 83
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