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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:

CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting
Lijing Wang, Aniruddha Adiga, Jiangzhuo Chen, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2022) Vol. 36, Iss. 11, pp. 12191-12199
Open Access | Times Cited: 53

Showing 1-25 of 53 citing articles:

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, et al.
IEEE Transactions on Knowledge and Data Engineering (2023) Vol. 36, Iss. 10, pp. 5388-5408
Open Access | Times Cited: 119
A Survey on Graph Representation Learning Methods
Shima Khoshraftar, Aijun An
ACM Transactions on Intelligent Systems and Technology (2023) Vol. 15, Iss. 1, pp. 1-55
Open Access | Times Cited: 55
EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting
Feng Xie, Zhong Zhang, Liang Li, et al.
Lecture notes in computer science (2023), pp. 469-485
Closed Access | Times Cited: 23
Dynamic Causal Graph Convolutional Network for Traffic Prediction
Junpeng Lin, Ziyue Li, Zhishuai Li, et al.
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (2023), pp. 1-8
Open Access | Times Cited: 17
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: 6
Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model
Xiaoyi Wang, Zhen Jin
PLoS Computational Biology (2025) Vol. 21, Iss. 1, pp. e1012738-e1012738
Open Access
Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges
Yang Ye, Abhishek Pandey, Carolyn E. Bawden, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Artificial intelligence for modelling infectious disease epidemics
Moritz U. G. Kraemer, Joseph L.-H. Tsui, Serina Chang, et al.
Nature (2025) Vol. 638, Iss. 8051, pp. 623-635
Closed Access
Machine Learning for Infectious Disease Risk Prediction: A Survey
Mutong Liu, Yang Liu, Jiming Liu
ACM Computing Surveys (2025)
Open Access
iPREDICT: AI enabled proactive pandemic prediction using biosensing wearable devices
Muhammad Sajid Riaz, Maria Shaukat, Tabish Saeed, et al.
Informatics in Medicine Unlocked (2024) Vol. 46, pp. 101478-101478
Open Access | Times Cited: 4
Dynamic Graph Representation Learning With Neural Networks: A Survey
Leshanshui Yang, Clément Chatelain, Sébastien Adam
IEEE Access (2024) Vol. 12, pp. 43460-43484
Open Access | Times Cited: 4
A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions
Feiyan Sun, Wenning Hao, Ao Zou, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 17, pp. 9919-9943
Closed Access | Times Cited: 4
MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks
Qi Cao, Renhe Jiang, Chuang Yang, et al.
Lecture notes in computer science (2023), pp. 453-468
Closed Access | Times Cited: 11
Hybrid multimodal fusion for graph learning in disease prediction
Ruomei Wang, Wei Guo, Yongjie Wang, et al.
Methods (2024) Vol. 229, pp. 41-48
Closed Access | Times Cited: 3
Backbone-based dynamic spatio-temporal graph neural network for epidemic forecasting
Junkai Mao, Yuexing Han, Gouhei Tanaka, et al.
Knowledge-Based Systems (2024) Vol. 296, pp. 111952-111952
Closed Access | Times Cited: 2
Sparse dynamic graph learning for district heat load forecasting
Yaohui Huang, Yuan Zhao, Zhijin Wang, et al.
Applied Energy (2024) Vol. 371, pp. 123685-123685
Open Access | Times Cited: 2
Multimodal vehicle trajectory prediction based on intention inference with lane graph representation
Yubin Chen, Yajie Zou, Yuanchang Xie, et al.
Expert Systems with Applications (2024) Vol. 262, pp. 125708-125708
Closed Access | Times Cited: 2
Big data technology in infectious diseases modeling, simulation, and prediction after the COVID-19 outbreak
Honghao Shi, Jingyuan Wang, Jiawei Cheng, et al.
Intelligent Medicine (2023) Vol. 3, Iss. 2, pp. 85-96
Open Access | Times Cited: 6
Interpretability for reliable, efficient, and self-cognitive DNNs: From theories to applications
Kang Xu, Jie Guo, Bin Song, et al.
Neurocomputing (2023) Vol. 545, pp. 126267-126267
Closed Access | Times Cited: 6
AI-Enabled Spatial-Temporal Mobility Awareness Service Migration for Connected Vehicles
Chenglong Wang, Jun Peng, Lin Cai, et al.
IEEE Transactions on Mobile Computing (2023) Vol. 23, Iss. 4, pp. 3274-3290
Closed Access | Times Cited: 5
Spatio-Temporal Meta Contrastive Learning
Jiabin Tang, Lianghao Xia, Jie Hu, et al.
(2023), pp. 2412-2421
Open Access | Times Cited: 5
MLFGCN: short-term residential load forecasting via graph attention temporal convolution network
Ding Feng, Dengao Li, Yu Zhou, et al.
Frontiers in Neurorobotics (2024) Vol. 18
Open Access | Times Cited: 1
Dynamic adaptive spatio–temporal graph network for COVID‐19 forecasting
Xiaojun Pu, Jiaqi Zhu, Yunkun Wu, et al.
CAAI Transactions on Intelligence Technology (2023) Vol. 9, Iss. 3, pp. 769-786
Open Access | Times Cited: 4
Epidemiology-aware Deep Learning for Infectious Disease Dynamics Prediction
Mutong Liu, Yang Liu, Jiming Liu
(2023), pp. 4084-4088
Closed Access | Times Cited: 4
Rethinking Causal Relationships Learning in Graph Neural Networks
Hang Gao, Chengyu Yao, Jiangmeng Li, et al.
Proceedings of the AAAI Conference on Artificial Intelligence (2024) Vol. 38, Iss. 11, pp. 12145-12154
Open Access | Times Cited: 1
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