Benoît Gaüzère
Impact in
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- Graph Theory and Algorithms
- Advanced Image and Video Retrieval Techniques
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- Computational Drug Discovery Methods
Papers in
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- Advanced Graph Neural Networks 10
- Machine Learning and Data Classification 3
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- Graph Theory and Algorithms 8
- Advanced Image and Video Retrieval Techniques 2
- Co-authors
- Luc Brun (6 shared papers)Pierre Héroux (3 shared papers)Sébastien Adam (3 shared papers)Paul Honeiné (5 shared papers)Laurent Joubert (3 shared papers)Muhammet Balcılar (2 shared papers)Vincent Tognetti (3 shared papers)Guillaume Hoffmann (1 shared paper)
In The Last Decade
Benoît Gaüzère
14 papers receiving 142 citations
Peers
Comparison fields: 5 of 43
- Computer Vision and Pattern Recognition 66
- Computational Theory and Mathematics 43
- Artificial Intelligence 64
- Signal Processing 17
- Process Chemistry and Technology 4
Countries citing papers authored by Benoît Gaüzère
This map shows the geographic impact of Benoît Gaüzère'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 Benoît Gaüzère with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benoît Gaüzère more than expected).
Fields of papers citing papers by Benoît Gaüzère
This network shows the impact of papers produced by Benoît Gaüzère. 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 Benoît Gaüzère. The network helps show where Benoît Gaüzère may publish in the future.
Co-authors
The 16 scholars most cited alongside Benoît Gaüzère, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 39 | |
| 2 | 2020 | 34 | |
| 3 | 2017 | 14 | |
| 4 | 2021 | 11 | |
| 5 | 2014 | 11 | |
| 6 | 2021 | 9 | |
| 7 | 2018 | 8 | |
| 8 | 2024 | 5 | |
| 9 | 2021 | 4 | |
| 10 | 2021 | 4 | |
| 11 | 2023 | 2 | |
| 12 | 2024 | 1 | |
| 13 | 2025 | 1 | |
| 14 | 2022 | 1 |
About Benoît Gaüzère
Benoît Gaüzère is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, Signal Processing and Analytical Chemistry, having authored 14 papers that have together received 144 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (10 papers), Graph Theory and Algorithms (8 papers), Computational Drug Discovery Methods (4 papers), Machine Learning and Data Classification (3 papers), Data Management and Algorithms (3 papers), Advanced Image and Video Retrieval Techniques (2 papers), Machine Learning in Materials Science (2 papers) and Spectroscopy and Chemometric Analyses (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (66 citations), Computational Theory and Mathematics (43 citations), Artificial Intelligence (64 citations), Signal Processing (17 citations) and Process Chemistry and Technology (4 citations). Benoît Gaüzère has collaborated with scholars based in France, Italy and Germany. Frequent co-authors include Luc Brun, Pierre Héroux, Sébastien Adam, Paul Honeiné, Laurent Joubert, Muhammet Balcılar, Vincent Tognetti, Guillaume Hoffmann, Sébastien Bougleux and David B. Blumenthal. Their work appears in journals such as Pattern Recognition Letters, Journal of Computational Chemistry, Electronics, Pattern Analysis and Applications and Pattern Recognition.
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.