Aurélien Decelle
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Computational Mathematics top 10%
Papers in
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- Model Reduction and Neural Networks 9
- Complex Network Analysis Techniques 7
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- Generative Adversarial Networks and Image Synthesis 14
- Co-authors
- L. Zdeborová (4 shared papers)Florent Krząkała (4 shared papers)Cristopher Moore (3 shared papers)Cyril Furtlehner (9 shared papers)Federico Ricci‐Tersenghi (3 shared papers)N. Aghanim (6 shared papers)Beatriz Seoane (16 shared papers)Y. Couder (1 shared paper)
In The Last Decade
Aurélien Decelle
41 papers receiving 1.2k citations
Aurélien Decelle's Hit Papers
Peers
Comparison fields: 5 of 104
- Statistical and Nonlinear Physics 640
- Computational Mathematics 13
- Statistics and Probability 148
- Condensed Matter Physics 131
- Artificial Intelligence 390
Countries citing papers authored by Aurélien Decelle
This map shows the geographic impact of Aurélien Decelle's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aurélien Decelle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aurélien Decelle more than expected).
Fields of papers citing papers by Aurélien Decelle
This network shows the impact of papers produced by Aurélien Decelle. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aurélien Decelle. The network helps show where Aurélien Decelle may publish in the future.
Co-authors
The 25 scholars most cited alongside Aurélien Decelle, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications Hit paper breakdown → | 2011 | 445 |
| 2 | 2011 | 243 | |
| 3 | 2021 | 77 | |
| 4 | 2009 | 54 | |
| 5 | 2014 | 48 | |
| 6 | 2020 | 40 | |
| 7 | 2010 | 32 | |
| 8 | 2018 | 32 | |
| 9 | 2017 | 28 | |
| 10 | 2022 | 27 | |
| 11 | 2023 | 15 | |
| 12 | 2021 | 15 | |
| 13 | 2022 | 15 | |
| 14 | 2016 | 14 | |
| 15 | 2015 | 13 | |
| 16 | 2009 | 13 | |
| 17 | 2019 | 13 | |
| 18 | 2023 | 13 | |
| 19 | 2021 | 11 | |
| 20 | 2022 | 11 |
About Aurélien Decelle
Aurélien Decelle is a scholar working on Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Artificial Intelligence, Condensed Matter Physics and Cognitive Neuroscience, having authored 44 papers that have together received 1.3k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (14 papers), Neural Networks and Applications (12 papers), Model Reduction and Neural Networks (9 papers), Theoretical and Computational Physics (8 papers), Complex Network Analysis Techniques (7 papers), Neural dynamics and brain function (6 papers), Gaussian Processes and Bayesian Inference (4 papers) and Galaxies: Formation, Evolution, Phenomena (4 papers). The work is most often cited by research in Statistical and Nonlinear Physics (640 citations), Computational Mathematics (13 citations), Statistics and Probability (148 citations), Condensed Matter Physics (131 citations) and Artificial Intelligence (390 citations). Aurélien Decelle has collaborated with scholars based in France, Spain and Italy. Frequent co-authors include L. Zdeborová, Florent Krząkała, Cristopher Moore, Cyril Furtlehner, Federico Ricci‐Tersenghi, N. Aghanim, Beatriz Seoane, Y. Couder, Antonin Eddi and Emmanuel Fort. Their work appears in journals such as Physical Review Letters, Physical review. E, Astronomy and Astrophysics, SciPost Physics and Europhysics Letters (EPL).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.