Christine Largeron
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Statistical and Nonlinear Physics top 10%
- Information Systems top 10%
- Computer Networks and Communications
- Co-authors
- Mathias GéryOsmar R. Zaı̈aneStéphane BonnevayAlessandra SalaSabrina GaitoRoberto InterdonatoMartin AtzmuellerVéronique Églin
- Topics
- Complex Network Analysis Techniques (7 papers)Information Retrieval and Search Behavior (5 papers)Data Management and Algorithms (5 papers)
In The Last Decade
Christine Largeron
28 papers receiving 280 citations
Peers
Comparison fields: 5 of 60
- Artificial Intelligence 140
- Computer Vision and Pattern Recognition 87
- Statistical and Nonlinear Physics 81
- Information Systems 60
- Computer Networks and Communications 37
Countries citing papers authored by Christine Largeron
This map shows the geographic impact of Christine Largeron'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 Christine Largeron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christine Largeron more than expected).
Fields of papers citing papers by Christine Largeron
This network shows the impact of papers produced by Christine Largeron. 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 Christine Largeron. The network helps show where Christine Largeron may publish in the future.
Co-authorship network of co-authors of Christine Largeron
This figure shows the co-authorship network connecting the top 25 collaborators of Christine Largeron. A scholar is included among the top collaborators of Christine Largeron based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Christine Largeron. Christine Largeron is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 14 | |
| 6 | 13 | |
| 7 | 4 | |
| 8 | 33 | |
| 9 | UJM at CLEF in Author Identification Notebook for PAN at CLEF 2014. | 7 |
| 10 | UJM at CLEF in Author Verification based on optimized classification trees | 5 |
| 11 | 16 | |
| 12 | 2 | |
| 13 | 4 | |
| 14 | 20 | |
| 15 | Modèle de Recherche d'Information Sociale Centré Utilisateur | 1 |
| 16 | ToTeM: une méthode de détection de communautés adaptées aux réseaux d'information | 1 |
| 17 | 19 | |
| 18 | 15 | |
| 19 | 2 | |
| 20 | 1 |
About Christine Largeron
Christine Largeron is a scholar working on Statistical and Nonlinear Physics, Signal Processing and Artificial Intelligence, having authored 33 papers that have together received 294 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (7 papers), Information Retrieval and Search Behavior (5 papers) and Data Management and Algorithms (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (81 citations), Artificial Intelligence (140 citations) and Computer Vision and Pattern Recognition (87 citations). Christine Largeron has collaborated with scholars based in France, Canada and Spain. Frequent co-authors include Mathias Géry, Osmar R. Zaı̈ane, Stéphane Bonnevay, Alessandra Sala, Sabrina Gaito, Roberto Interdonato, Martin Atzmueller, Véronique Églin, Rushed Kanawati and Christophe García. Their work appears in journals such as PLoS ONE, IEEE Access 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.