Aurélien Decelle
Impact in
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- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Model Reduction and Neural Networks
- Statistics and Probability top 5%
- Random Matrices and Applications
Papers in
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- Model Reduction and Neural Networks 9
- Complex Network Analysis Techniques 6
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- Neural Networks and Applications 10
- Co-authors
- Lenka Zdeborová (3 shared papers)Florent Krząkała (3 shared papers)Cristopher Moore (2 shared papers)Cyril Furtlehner (9 shared papers)Federico Ricci‐Tersenghi (3 shared papers)Beatriz Seoane (13 shared papers)Nabila Aghanim (4 shared papers)Emmanuel Fort (1 shared paper)
In The Last Decade
Aurélien Decelle
35 papers receiving 1.0k citations
Aurélien Decelle's Hit Papers
Peers
Comparison fields: 5 of 98
- Statistical and Nonlinear Physics 585
- Statistics and Probability 129
- Computational Mathematics 9
- Condensed Matter Physics 129
- Artificial Intelligence 342
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 38 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 | 384 |
| 2 | 2011 | 221 | |
| 3 | 2021 | 75 | |
| 4 | 2009 | 50 | |
| 5 | 2014 | 43 | |
| 6 | 2020 | 35 | |
| 7 | 2018 | 31 | |
| 8 | 2010 | 31 | |
| 9 | 2017 | 25 | |
| 10 | 2021 | 14 | |
| 11 | 2022 | 13 | |
| 12 | 2016 | 13 | |
| 13 | 2019 | 13 | |
| 14 | 2023 | 12 | |
| 15 | 2015 | 12 | |
| 16 | 2009 | 11 | |
| 17 | 2021 | 11 | |
| 18 | 2023 | 10 | |
| 19 | 2015 | 9 | |
| 20 | 2022 | 8 |
About Aurélien Decelle
Aurélien Decelle is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence, Computer Vision and Pattern Recognition, Condensed Matter Physics and Cognitive Neuroscience, having authored 38 papers that have together received 1.1k indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (13 papers), Neural Networks and Applications (10 papers), Theoretical and Computational Physics (9 papers), Model Reduction and Neural Networks (9 papers), Neural dynamics and brain function (6 papers), Complex Network Analysis Techniques (6 papers), Markov Chains and Monte Carlo Methods (3 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (585 citations), Statistics and Probability (129 citations), Computational Mathematics (9 citations), Condensed Matter Physics (129 citations) and Artificial Intelligence (342 citations). Aurélien Decelle has collaborated with scholars based in France, Spain and Italy. Frequent co-authors include Lenka Zdeborová, Florent Krząkała, Cristopher Moore, Cyril Furtlehner, Federico Ricci‐Tersenghi, Beatriz Seoane, Nabila Aghanim, Emmanuel Fort, Y. Couder and Antonin Eddi. Their work appears in journals such as Physical review. E, Physical Review Letters, 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.