Nicolas Tremblay
- Artificial Intelligence top 5%
- Statistical and Nonlinear Physics top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Control and Systems Engineering top 10%
- Co-authors
- Pierre BorgnatRémi GribonvalPierre VandergheynstGilles PuyMichaël FlorianMichael MahutAndreas LoukasLuc Le Magoarou
- Topics
- Advanced Graph Neural Networks (11 papers)Complex Network Analysis Techniques (11 papers)Bioinformatics and Genomic Networks (6 papers)
- Partner nations
- FranceCanadaSwitzerland
In The Last Decade
Nicolas Tremblay
42 papers receiving 649 citations
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 303
- Statistical and Nonlinear Physics 220
- Computer Vision and Pattern Recognition 105
- Molecular Biology 88
- Control and Systems Engineering 85
Countries citing papers authored by Nicolas Tremblay
This map shows the geographic impact of Nicolas Tremblay'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 Nicolas Tremblay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicolas Tremblay more than expected).
Fields of papers citing papers by Nicolas Tremblay
This network shows the impact of papers produced by Nicolas Tremblay. 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 Nicolas Tremblay. The network helps show where Nicolas Tremblay may publish in the future.
Co-authorship network of co-authors of Nicolas Tremblay
This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Tremblay. A scholar is included among the top collaborators of Nicolas Tremblay 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 Nicolas Tremblay. Nicolas Tremblay 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 | 0 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 3 | |
| 6 | 1 | |
| 7 | 17 | |
| 8 | 6 | |
| 9 | 9 | |
| 10 | 40 | |
| 11 | 2 | |
| 12 | 42 | |
| 13 | 9 | |
| 14 | 9 | |
| 15 | 95 | |
| 16 | 3 | |
| 17 | 9 | |
| 18 | Comparison of Assignment Methods for Simulation-Based Dynamic-Equilibrium Traffic Assignment | 12 |
| 19 | Space-Time Queues and Dynamic Traffic Assignment: A Model, Algorithm and Applications | 17 |
| 20 | Parler naturellement au figuré / Figures de pensée, figures de discours, de Danielle Forget, Nota bene, « Langue et pratiques discursives », 186 p. | 1 |
About Nicolas Tremblay
Nicolas Tremblay is a scholar working on Statistical and Nonlinear Physics, Statistics and Probability and Software, having authored 44 papers that have together received 685 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (11 papers), Complex Network Analysis Techniques (11 papers) and Bioinformatics and Genomic Networks (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (220 citations), Transportation (70 citations) and Artificial Intelligence (303 citations). Nicolas Tremblay has collaborated with scholars based in France, Canada and Switzerland. Frequent co-authors include Pierre Borgnat, Rémi Gribonval, Pierre Vandergheynst, Gilles Puy, Michaël Florian, Michael Mahut, Andreas Loukas, Luc Le Magoarou, Hiroshi Esaki and Romain Fontugne. Their work appears in journals such as PLoS ONE, Journal of Colloid and Interface Science and IEEE Transactions on Signal Processing.
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.