Jean-Gabriel Young
- Statistical and Nonlinear Physics top 5%
- Molecular Biology
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Sociology and Political Science
- Co-authors
- M. E. J. NewmanGeorge T. CantwellLouis J. DubéLaurent Hébert‐DufresneAntoine AllardAlice PataniaGiovanni PetriFrancesco Vaccarino
- Topics
- Complex Network Analysis Techniques (25 papers)Opinion Dynamics and Social Influence (13 papers)Evolutionary Game Theory and Cooperation (5 papers)
- Cited by
- Statistical and Nonlinear PhysicsModeling and SimulationExperimental and Cognitive Psychology
- Partner nations
- United StatesCanadaSpain
In The Last Decade
Jean-Gabriel Young
34 papers receiving 337 citations
Peers
Comparison fields: 5 of 72
- Statistical and Nonlinear Physics 220
- Molecular Biology 56
- Artificial Intelligence 56
- Computational Theory and Mathematics 42
- Sociology and Political Science 36
Countries citing papers authored by Jean-Gabriel Young
This map shows the geographic impact of Jean-Gabriel Young'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 Jean-Gabriel Young with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean-Gabriel Young more than expected).
Fields of papers citing papers by Jean-Gabriel Young
This network shows the impact of papers produced by Jean-Gabriel Young. 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 Jean-Gabriel Young. The network helps show where Jean-Gabriel Young may publish in the future.
Co-authorship network of co-authors of Jean-Gabriel Young
This figure shows the co-authorship network connecting the top 25 collaborators of Jean-Gabriel Young. A scholar is included among the top collaborators of Jean-Gabriel Young 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 Jean-Gabriel Young. Jean-Gabriel Young is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 14 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 4 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | Robust Bayesian inference of network structure from unreliable data | 2 |
| 10 | 1 | |
| 11 | 18 | |
| 12 | 1 | |
| 13 | 3 | |
| 14 | 50 | |
| 15 | 5 | |
| 16 | 4 | |
| 17 | 17 | |
| 18 | 5 | |
| 19 | Random networks with arbitrary k-core structure. | 1 |
| 20 | 18 |
About Jean-Gabriel Young
Jean-Gabriel Young is a scholar working on Statistical and Nonlinear Physics, Modeling and Simulation and Statistics and Probability, having authored 37 papers that have together received 341 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (25 papers), Opinion Dynamics and Social Influence (13 papers) and Evolutionary Game Theory and Cooperation (5 papers). The work is most often cited by research in Statistical and Nonlinear Physics (220 citations), Modeling and Simulation (23 citations) and Experimental and Cognitive Psychology (35 citations). Jean-Gabriel Young has collaborated with scholars based in United States, Canada and Spain. Frequent co-authors include M. E. J. Newman, George T. Cantwell, Louis J. Dubé, Laurent Hébert‐Dufresne, Antoine Allard, Alice Patania, Giovanni Petri, Francesco Vaccarino, Guillaume St-Onge and Fernanda S. Valdovinos. Their work appears in journals such as Physical Review Letters, Nature Communications and PLoS ONE.
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.