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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:

Inferring connectivity in networked dynamical systems: Challenges using Granger causality
Bethany Lusch, Pedro D. Maia, J. Nathan Kutz
Physical review. E (2016) Vol. 94, Iss. 3
Closed Access | Times Cited: 44

Showing 1-25 of 44 citing articles:

Granger Causality: A Review and Recent Advances
Ali Shojaie, Emily B. Fox
Annual Review of Statistics and Its Application (2021) Vol. 9, Iss. 1, pp. 289-319
Open Access | Times Cited: 219
Neural Granger Causality
Alex Tank, Ian Covert, Nicholas J. Foti, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2021), pp. 1-1
Open Access | Times Cited: 147
The effect of uncertainty on the precious metals market: New insights from Transfer Entropy and Neural Network VAR
Toan Luu Duc Huynh
Resources Policy (2020) Vol. 66, pp. 101623-101623
Closed Access | Times Cited: 99
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia
Caroline L. Alves, Aruane M. Pineda, Kirstin Roster, et al.
Journal of Physics Complexity (2022) Vol. 3, Iss. 2, pp. 025001-025001
Open Access | Times Cited: 45
Data-driven causal analysis of observational biological time series
Alex E. Yuan, Wenying Shou
eLife (2022) Vol. 11
Open Access | Times Cited: 37
Neural Granger Causality for Nonlinear Time Series
Alex Tank, Ian Covert, Nicholas J. Foti, et al.
arXiv (Cornell University) (2018)
Closed Access | Times Cited: 73
Dynamic time warp versus vector autoregression models for network analyses of psychological processes
Florentine H. S. van der Does, W. van Eeden, Laura F. Bringmann, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Network structure from a characterization of interactions in complex systems
Thorsten Rings, Timo Bröhl, Klaus Lehnertz
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 16
Embedding entropy: a nonlinear measure of dynamical causality
Jifan Shi, Luonan Chen, Kazuyuki Aihara
Journal of The Royal Society Interface (2022) Vol. 19, Iss. 188
Open Access | Times Cited: 13
Nonlinear causal network learning via Granger causality based on extreme support vector regression
Guanxue Yang, Weiwei Hu, Lidong He, et al.
Chaos An Interdisciplinary Journal of Nonlinear Science (2024) Vol. 34, Iss. 2
Open Access | Times Cited: 2
Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data
Matthieu Gilson, Adrià Tauste Campo, Xing Chen, et al.
Network Neuroscience (2017) Vol. 1, Iss. 4, pp. 357-380
Open Access | Times Cited: 23
Echo state network models for nonlinear Granger causality
Andrea Duggento, Maria Guerrisi, Nicola Toschi
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2021) Vol. 379, Iss. 2212
Open Access | Times Cited: 16
Interpretable Multi-Scale Neural Network for Granger Causality Discovery
Chenchen Fan, Zhaoran Wang, Yahong Zhang, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023)
Closed Access | Times Cited: 5
Echo State Network models for nonlinear Granger causality
Andrea Duggento, Maria Guerrisi, Nicola Toschi
bioRxiv (Cold Spring Harbor Laboratory) (2019)
Open Access | Times Cited: 15
Learning System Descriptions for Cyber-Physical Systems
Alexander Diedrich, Lukas Moddemann, Oliver Niggemann
IFAC-PapersOnLine (2024) Vol. 58, Iss. 4, pp. 628-633
Open Access | Times Cited: 1
Graph-Variate Signal Analysis
Keith Smith, Loukianos Spyrou, Javier Escudero
IEEE Transactions on Signal Processing (2018) Vol. 67, Iss. 2, pp. 293-305
Open Access | Times Cited: 12
Inferring causal networks of dynamical systems through transient dynamics and perturbation
George Stepaniants, Bingni W. Brunton, J. Nathan Kutz
Physical review. E (2020) Vol. 102, Iss. 4
Open Access | Times Cited: 11
On Causal Discovery With Convergent Cross Mapping
Kurt Butler, Guanchao Feng, Petar M. Djurić
IEEE Transactions on Signal Processing (2023) Vol. 71, pp. 2595-2607
Closed Access | Times Cited: 3
Granger Causal Inference for Interpretable Traffic Prediction
Lei Zhang, Kaiqun Fu, Taoran Ji, et al.
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (2022), pp. 1645-1651
Closed Access | Times Cited: 5
Impact of local network characteristics on network reconstruction
Gloria Cecchini, Rok Cestnik, Arkady Pikovsky
Physical review. E (2021) Vol. 103, Iss. 2
Open Access | Times Cited: 6
Exact Inference of Causal Relations in Dynamical Systems
Zsigmond Benkő, Ádám Zlatniczki, Dániel Fabó, et al.
arXiv (Cornell University) (2018)
Closed Access | Times Cited: 5
Recurrent neural networks for reconstructing complex directed brain connectivity
Andrea Duggento, Maria Guerrisi, Nicola Toschi
(2019), pp. 6418-6421
Closed Access | Times Cited: 5
Granger Causality: A Review and Recent Advances
Ali Shojaie, Emily B. Fox
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 5
Statistics of Growing Chemical Network Originating from One Molecule Species and Activated by Low-Temperature Plasma
Yasutaka Mizui, Shigeyuki Miyagi, Osamu Sakai
Studies in computational intelligence (2021), pp. 398-409
Closed Access | Times Cited: 4
Bayesian inference of causal relations between dynamical systems
Zsigmond Benkő, Ádám Zlatniczki, Marcell Stippinger, et al.
Chaos Solitons & Fractals (2024) Vol. 185, pp. 115142-115142
Open Access
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