Céline Rouveirol
- Artificial Intelligence top 5%
- Molecular Biology
- Information Systems top 10%
- Statistical and Nonlinear Physics top 10%
- Computational Theory and Mathematics top 10%
- Co-authors
- Claire NédellecRushed KanawatiMohamed ElatiEmmanuel BarillotFrançois RadvanyiMichèle SébagEric ViaraPhilippe Hupé
- Topics
- Bioinformatics and Genomic Networks (7 papers)Gene expression and cancer classification (7 papers)Logic, Reasoning, and Knowledge (6 papers)
- Partner nations
- FranceItalyUnited Kingdom
In The Last Decade
Céline Rouveirol
27 papers receiving 454 citations
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 261
- Molecular Biology 151
- Information Systems 94
- Statistical and Nonlinear Physics 88
- Computational Theory and Mathematics 56
Countries citing papers authored by Céline Rouveirol
This map shows the geographic impact of Céline Rouveirol'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 Céline Rouveirol with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Céline Rouveirol more than expected).
Fields of papers citing papers by Céline Rouveirol
This network shows the impact of papers produced by Céline Rouveirol. 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 Céline Rouveirol. The network helps show where Céline Rouveirol may publish in the future.
Co-authorship network of co-authors of Céline Rouveirol
This figure shows the co-authorship network connecting the top 25 collaborators of Céline Rouveirol. A scholar is included among the top collaborators of Céline Rouveirol 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 Céline Rouveirol. Céline Rouveirol is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 19 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 10 | |
| 6 | 90 | |
| 7 | 8 | |
| 8 | 3 | |
| 9 | 32 | |
| 10 | Constraint-based Learning of Long Relational Concepts | 3 |
| 11 | Lazy propositionalisation for relational learning | 10 |
| 12 | 14 | |
| 13 | Machine learning: ECML-98 : 10th European Conference on Machine Learning, Chemnitz, Germany, April 21-23, 1998 : proceedings | 12 |
| 14 | Proceedings of the 10th European Conference on Machine Learning | 22 |
| 15 | 1 | |
| 16 | 34 | |
| 17 | Bottom-up generalisation in inductive logic programming | 1 |
| 18 | Extensions of Inversion of Resolution Applied to Theory Completion | 31 |
| 19 | Semantic model for induction of first order theories | 3 |
| 20 | Saturation: postponing choices when inverting resolution | 1 |
About Céline Rouveirol
Céline Rouveirol is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Statistical and Nonlinear Physics, having authored 28 papers that have together received 477 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (7 papers), Gene expression and cancer classification (7 papers) and Logic, Reasoning, and Knowledge (6 papers). The work is most often cited by research in Artificial Intelligence (261 citations), Statistical and Nonlinear Physics (88 citations) and Information Systems (94 citations). Céline Rouveirol has collaborated with scholars based in France, Italy and United Kingdom. Frequent co-authors include Claire Nédellec, Rushed Kanawati, Mohamed Elati, Emmanuel Barillot, François Radvanyi, Michèle Sébag, Eric Viara, Philippe Hupé, Nicolas Stransky and Monique Bolotin‐Fukuhara. Their work appears in journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Machine Learning.
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.