Jean-Gabriel Young

656 total citations
37 papers, 341 citations indexed

About

Jean-Gabriel Young is a scholar working on Statistical and Nonlinear Physics, Sociology and Political Science and Artificial Intelligence. According to data from OpenAlex, Jean-Gabriel Young has authored 37 papers receiving a total of 341 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Statistical and Nonlinear Physics, 8 papers in Sociology and Political Science and 8 papers in Artificial Intelligence. Recurrent topics in Jean-Gabriel Young's work include Complex Network Analysis Techniques (25 papers), Opinion Dynamics and Social Influence (13 papers) and Evolutionary Game Theory and Cooperation (5 papers). Jean-Gabriel Young is often cited by papers focused on Complex Network Analysis Techniques (25 papers), Opinion Dynamics and Social Influence (13 papers) and Evolutionary Game Theory and Cooperation (5 papers). Jean-Gabriel Young collaborates with scholars based in United States, Canada and Spain. Jean-Gabriel Young's co-authors include M. E. J. Newman, George T. Cantwell, Louis J. Dubé, Laurent Hébert‐Dufresne, Antoine Allard, Alice Patania, Giovanni Petri, Guillaume St-Onge, Francesco Vaccarino and Fernanda S. Valdovinos and has published in prestigious journals such as Physical Review Letters, Nature Communications and PLoS ONE.

In The Last Decade

Jean-Gabriel Young

34 papers receiving 337 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jean-Gabriel Young United States 11 220 56 56 42 36 37 341
Rossana Mastrandrea Italy 10 286 1.3× 40 0.7× 60 1.1× 31 0.7× 50 1.4× 18 546
Giulia Cencetti Italy 8 184 0.8× 29 0.5× 36 0.6× 28 0.7× 33 0.9× 17 323
Caterina De Bacco Germany 11 332 1.5× 148 2.6× 94 1.7× 34 0.8× 29 0.8× 33 526
H. Wang Netherlands 10 226 1.0× 47 0.8× 30 0.5× 74 1.8× 22 0.6× 21 390
Maxime Lucas Italy 9 139 0.6× 34 0.6× 27 0.5× 33 0.8× 23 0.6× 17 268
Emanuele Cozzo Spain 11 360 1.6× 80 1.4× 34 0.6× 26 0.6× 63 1.8× 20 487
Guillaume St-Onge Canada 11 175 0.8× 29 0.5× 23 0.4× 14 0.3× 40 1.1× 20 282
Luca Gallo Italy 12 237 1.1× 62 1.1× 53 0.9× 43 1.0× 21 0.6× 27 574
George T. Cantwell United States 8 210 1.0× 39 0.7× 57 1.0× 24 0.6× 31 0.9× 15 335
Mark E. Dickison United States 5 345 1.6× 31 0.6× 28 0.5× 16 0.4× 68 1.9× 6 487

Countries citing papers authored by Jean-Gabriel Young

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Young, Jean-Gabriel, et al.. (2025). Symmetry-driven embedding of networks in hyperbolic space. Communications Physics. 8(1).
2.
Hébert‐Dufresne, Laurent, Yong‐Yeol Ahn, Antoine Allard, et al.. (2025). One pathogen does not an epidemic make: a review of interacting contagions, diseases, beliefs, and stories. PubMed. 2(1). 26–26. 1 indexed citations
3.
Hébert‐Dufresne, Laurent, et al.. (2025). Governance as a complex, networked, democratic, satisfiability problem. PubMed. 2(1). 14–14.
4.
Young, Jean-Gabriel, et al.. (2024). The simpliciality of higher-order networks. EPJ Data Science. 13(1). 14 indexed citations
5.
Thompson, William, et al.. (2024). Reconstructing networks from simple and complex contagions. Physical review. E. 110(4). L042301–L042301. 1 indexed citations
6.
Patania, Alice, Antoine Allard, & Jean-Gabriel Young. (2023). Exact and rapid linear clustering of networks with dynamic programming. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 479(2275). 5 indexed citations
7.
Young, Jean-Gabriel, et al.. (2023). Hypergraph reconstruction from uncertain pairwise observations. Scientific Reports. 13(1). 21364–21364. 4 indexed citations
8.
Bacco, Caterina De, Tracy M. Sweet, Jean-Gabriel Young, et al.. (2023). Latent network models to account for noisy, multiply reported social network data. Journal of the Royal Statistical Society Series A (Statistics in Society). 186(3). 355–375. 10 indexed citations
9.
Garland, Joshua, et al.. (2023). Correction: Impact and dynamics of hate and counter speech online. EPJ Data Science. 12(1). 1 indexed citations
10.
Young, Jean-Gabriel, Laurent Hébert‐Dufresne, Antoine Allard, & Louis J. Dubé. (2020). Constrained growth of complex scale-independent systems. Corpus Université Laval (Université Laval). 1 indexed citations
11.
Young, Jean-Gabriel, George T. Cantwell, & M. E. J. Newman. (2020). Robust Bayesian inference of network structure from unreliable data. arXiv (Cornell University). 2 indexed citations
12.
St-Onge, Guillaume, Jean-Gabriel Young, Laurent Hébert‐Dufresne, & Louis J. Dubé. (2019). Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm. Computer Physics Communications. 240. 30–37. 18 indexed citations
13.
Young, Jean-Gabriel, Guillaume St-Onge, Patrick Desrosiers, & Louis J. Dubé. (2018). Universality of the stochastic block model. Physical review. E. 98(3). 13 indexed citations
14.
Young, Jean-Gabriel, et al.. (2018). Exact analytical solution of irreversible binary dynamics on networks. Physical review. E. 97(3). 32302–32302. 1 indexed citations
15.
Hébert‐Dufresne, Laurent, Antoine Allard, Pierre‐André Noël, Jean-Gabriel Young, & Eric Libby. (2017). Strategic tradeoffs in competitor dynamics on adaptive networks. PubMed Central. 3 indexed citations
16.
Young, Jean-Gabriel, Giovanni Petri, Francesco Vaccarino, & Alice Patania. (2017). Construction of and efficient sampling from the simplicial configuration model. Physical review. E. 96(3). 32312–32312. 50 indexed citations
17.
Hébert‐Dufresne, Laurent, et al.. (2015). Complex networks as an emerging property of hierarchical preferential attachment. Physical Review E. 92(6). 62809–62809. 4 indexed citations
18.
Allard, Antoine, Laurent Hébert‐Dufresne, Jean-Gabriel Young, & Louis J. Dubé. (2014). Coexistence of phases and the observability of random graphs. Physical Review E. 89(2). 22801–22801. 5 indexed citations
19.
Hébert‐Dufresne, Laurent, Antoine Allard, Jean-Gabriel Young, & Louis J. Dubé. (2013). Random networks with arbitrary k-core structure.. arXiv (Cornell University). 1 indexed citations
20.
Hébert‐Dufresne, Laurent, Antoine Allard, Jean-Gabriel Young, & Louis J. Dubé. (2013). Percolation on random networks with arbitraryk-core structure. Physical Review E. 88(6). 62820–62820. 18 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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