Stéphane Bonnevay

418 total citations
22 papers, 210 citations indexed

About

Stéphane Bonnevay is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Transportation. According to data from OpenAlex, Stéphane Bonnevay has authored 22 papers receiving a total of 210 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 4 papers in Computational Theory and Mathematics and 3 papers in Transportation. Recurrent topics in Stéphane Bonnevay's work include Advanced Multi-Objective Optimization Algorithms (3 papers), Transportation Planning and Optimization (3 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Stéphane Bonnevay is often cited by papers focused on Advanced Multi-Objective Optimization Algorithms (3 papers), Transportation Planning and Optimization (3 papers) and Metaheuristic Optimization Algorithms Research (3 papers). Stéphane Bonnevay collaborates with scholars based in France, United Kingdom and Malta. Stéphane Bonnevay's co-authors include Christine Largeron, Michel Lamure, Valérie Bourdès, Thomas Bachelot, David Pérol, Min Hane Aung, Paulo Lisböa, Sylvie Négrier, Sylvie Chabaud and Nicolas Danchin and has published in prestigious journals such as Information Sciences, Neural Networks and Eurosurveillance.

In The Last Decade

Stéphane Bonnevay

21 papers receiving 198 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Stéphane Bonnevay France 10 65 63 52 33 16 22 210
Ahlame Douzal-Chouakria France 8 34 0.5× 5 0.1× 111 2.1× 18 0.5× 2 0.1× 13 254
Vishesh Karwa United States 7 16 0.2× 16 0.3× 179 3.4× 16 0.5× 15 307
Xiusi Chen United States 8 93 1.4× 45 0.7× 101 1.9× 3 0.1× 22 264
Calvin S. H. Hoi Canada 8 36 0.6× 16 0.3× 111 2.1× 7 0.2× 25 1.6× 9 214
Leonardo C. T. Bezerra Brazil 9 14 0.2× 15 0.2× 145 2.8× 109 3.3× 19 240
Binggui Zhou Macao 7 12 0.2× 10 0.2× 72 1.4× 2 0.1× 20 1.3× 17 229
Junyi Gao China 12 13 0.2× 17 0.3× 314 6.0× 10 0.3× 148 9.3× 30 469
Marek Kvet Slovakia 8 8 0.1× 37 0.6× 21 0.4× 14 0.4× 56 218
Milan Šimůnek Czechia 7 12 0.2× 10 0.2× 73 1.4× 51 1.5× 1 0.1× 20 158

Countries citing papers authored by Stéphane Bonnevay

Since Specialization
Citations

This map shows the geographic impact of Stéphane Bonnevay'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 Stéphane Bonnevay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stéphane Bonnevay more than expected).

Fields of papers citing papers by Stéphane Bonnevay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stéphane Bonnevay. 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 Stéphane Bonnevay. The network helps show where Stéphane Bonnevay may publish in the future.

Co-authorship network of co-authors of Stéphane Bonnevay

This figure shows the co-authorship network connecting the top 25 collaborators of Stéphane Bonnevay. A scholar is included among the top collaborators of Stéphane Bonnevay 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 Stéphane Bonnevay. Stéphane Bonnevay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Bonnevay, Stéphane, et al.. (2022). Nonparametric neural topic modeling for customer insight extraction about the tire industry. 2022 International Joint Conference on Neural Networks (IJCNN). 2936. 1–9. 1 indexed citations
2.
Béresniak, Ariel, Caterina Rizzo, John Oxford, et al.. (2019). Cost-effectiveness of public health interventions against human influenza pandemics in France: a methodological contribution from the FLURESP European Commission project. European Journal of Public Health. 30(1). 43–49. 3 indexed citations
3.
Bonnevay, Stéphane, et al.. (2017). Dynamic Bayesian networks with Gaussian mixture models for short-term passenger flow forecasting. 1–8. 14 indexed citations
4.
Velcin, Julien, et al.. (2017). Evolutionary clustering for categorical data using parametric links among multinomial mixture models. Econometrics and Statistics. 3. 141–159. 3 indexed citations
5.
Bonnevay, Stéphane, et al.. (2017). A dynamic Bayesian network approach to forecast short-term urban rail passenger flows with incomplete data. Transportation research procedia. 26. 53–61. 40 indexed citations
6.
Bonnevay, Stéphane, Pierrick Legrand, Nicolas Monmarché, Évelyne Lutton, & Marc Schoenauer. (2016). Artificial Evolution. Lecture notes in computer science. 1 indexed citations
7.
Bonnevay, Stéphane, et al.. (2016). Short-Term Urban Rail Passenger Flow Forecasting: A Dynamic Bayesian Network Approach. 1034–1039. 24 indexed citations
8.
Bonnevay, Stéphane, et al.. (2016). Comparison of two metaheuristics to solve a 2-D cutting stock problem with set-up cost in the paper industry. 5(1). 31–31. 2 indexed citations
9.
Napoli, Christian, Massimo Fabiani, Caterina Rizzo, et al.. (2015). Assessment of human influenza pandemic scenarios in Europe. Eurosurveillance. 20(7). 29–38. 14 indexed citations
10.
Bonnevay, Stéphane, et al.. (2013). A hybrid evolutionary approach with search strategy adaptation for mutiobjective optimization. 631–638. 2 indexed citations
11.
Bonnevay, Stéphane, et al.. (2012). Hybrid Metaheuristics based on MOEA/D for 0/1 multiobjective knapsack problems: A comparative study. 14. 1–8. 12 indexed citations
12.
Bourdès, Valérie, et al.. (2011). Prediction of persistence of combined evidence-based cardiovascular medications in patients with acute coronary syndrome after hospital discharge using neural networks. Medical & Biological Engineering & Computing. 49(8). 947–955. 17 indexed citations
13.
Bonnevay, Stéphane, et al.. (2011). A hybrid evolutionary metaheuristics (HEMH) applied on 0/1 multiobjective knapsack problems. 497–504. 7 indexed citations
14.
Lisböa, Paulo, Terence A. Etchells, Ian H. Jarman, et al.. (2008). Time-to-event analysis with artificial neural networks: An integrated analytical and rule-based study for breast cancer. Neural Networks. 21(2-3). 414–426. 14 indexed citations
15.
Etchells, Terence A., Ian H. Jarman, Min Hane Aung, et al.. (2007). Development of a Rule Based Prognostic Tool for HER 2 Positive Breast Cancer Patients. Conference proceedings. 2007. 5416–5419. 2 indexed citations
16.
Bonnevay, Stéphane, David M. Dror, Gérard Duru, & Michel Lamure. (2007). A Model of Microinsurance and Reinsurance. 4 indexed citations
17.
Bonnevay, Stéphane, Paulo Lisböa, Min Hane Aung, et al.. (2007). Breast Cancer Predictions by Neural Networks Analysis: a Comparison with Logistic Regression. Conference proceedings. 20. 5424–5427. 10 indexed citations
18.
Bonnevay, Stéphane, et al.. (2006). Agent-based simulation of coalition formation in cooperative games. IEEE/WIC/ACM International Conference on Intelligent Agent Technology. 1. 136–139. 9 indexed citations
19.
Batrancourt, Bénédicte, Stéphane Bonnevay, Michel Lamure, & Richard Lévy. (2003). An anatomofunctional brain knowledge. 2. 1269–1276.
20.
Bonnevay, Stéphane, et al.. (1999). A pretopological approach for structuring data in non-metric spaces. Electronic Notes in Discrete Mathematics. 2. 1–9. 4 indexed citations

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.

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