Jean-Gabriel Young

Jean-Gabriel Young
Nationality	Canadian
Occupation	Professor of Complex Systems
Employer	University of Vermont
Known for	Network science, computational epidemiology, statistical inference on networks
Alma mater	Université Laval

Jean-Gabriel Young is a researcher and professor working in the field of complex systems, with an emphasis on network science and computational epidemiology. He holds a faculty position at the University of Vermont (UVM), where he studies the structure and dynamics of networks, the mathematics of contagion processes, and methods for inferring hidden features of large interconnected systems. His work draws together statistical physics, applied mathematics, and computational methods to address questions about how diseases, information, and social behaviors spread through networked populations.

Education

Jean-Gabriel Young completed his graduate training in physics, specializing in the study of complex networks and statistical mechanics as applied to social and biological systems. His doctoral research focused on developing quantitative tools for analyzing the structure of networks and modeling the processes, such as epidemics and information cascades, that unfold on them. This training established the methodological foundation for his subsequent research program, which spans statistical inference, network theory, and mathematical epidemiology.

Career

Young joined the faculty of the University of Vermont, where he holds an appointment associated with the university's interdisciplinary programs in complex systems and data science. At UVM, he has built a research group focused on the mathematical and computational study of networks, contributing to the university's broader reputation in complex systems science, an area supported by UVM's Vermont Complex Systems Center.

As a principal investigator, Young has secured external funding to support his research program. He received a National Science Foundation (NSF) award for a collaborative research project titled "HNDS-R: Altruistic stress, economic networks, and endogenous organizational change," administered through the University of Vermont and State Agricultural College, with funding of $114,810 beginning July 30, 2024. He also received an NSF conference grant supporting the "Contagion on Complex Social Systems 2023" conference, administered through the same institution, with funding of $47,838 beginning June 15, 2023. Together these two NSF awards total $162,648 in direct research support.

Young's academic output, as tracked by Semantic Scholar, comprises 64 published papers that have collectively received 2,727 citations, yielding an h-index of 18. These metrics place him among active contributors to the network science and computational epidemiology literature, with a publication record spanning journals such as Physical Review E, the Proceedings of the Royal Society A, Royal Society Open Science, Communications Physics, EPJ Data Science, and npj Complexity.

Research

Young's research addresses several interconnected themes within the broader field of network science: the statistical inference of network structure, the dynamics of contagion processes (including both biological epidemics and the spread of social behaviors or beliefs), and methods for embedding and clustering networks to reveal their underlying organization.

Contagion and epidemic modeling

A significant portion of Young's work concerns the mathematics of how diseases and disease-like processes spread through networked populations. In a 2025 review published in npj Complexity, titled "One pathogen does not an epidemic make: a review of interacting contagions, diseases, beliefs, and stories," Young and collaborators examined how multiple contagion processes, whether biological pathogens, competing narratives, or co-circulating beliefs, interact with one another rather than spreading in isolation. This paper has been cited five times as of its indexing.

Young has also contributed methodological advances to epidemic forecasting. His 2025 preprint "Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions," posted on arXiv, develops mathematical tools using probability generating functions to assess how sensitive epidemic forecasts are to underlying model assumptions and network structure. A related 2025 arXiv preprint, "Message passing for epidemiological interventions on networks with loops," extends message-passing techniques, a class of approximation methods originally developed in statistical physics and computer science, to epidemiological models on networks containing loops, a structural feature that complicates many standard analytical approaches.

Earlier work in this vein includes the 2023 paper "Accurately summarizing an outbreak using epidemiological models takes time," published in Royal Society Open Science, which investigates the temporal limitations of epidemiological models in producing reliable outbreak summaries; this paper has accumulated seven citations. Young also co-authored the 2024 paper "Reconstructing networks from simple and complex contagions," published in Physical Review E, which addresses the inverse problem of inferring network structure from observed patterns of contagion spread, distinguishing between simple contagions (such as many infectious diseases) and complex contagions (such as behaviors that require reinforcement from multiple sources before adoption).

Network structure and inference

Beyond contagion dynamics, Young has contributed to methods for analyzing network structure directly. His 2023 paper "Exact and rapid linear clustering of networks with dynamic programming," published in the Proceedings of the Royal Society A, introduces a dynamic programming approach for clustering networks along a linear or sequential ordering, achieving exact solutions with improved computational efficiency; the paper has been cited seven times.

In 2024, Young co-authored "Symmetry-driven embedding of networks in hyperbolic space," published in Communications Physics, which proposes techniques for representing networks in hyperbolic geometric space by exploiting symmetries in network structure, an approach relevant to visualizing and analyzing large-scale networks with hierarchical or scale-free properties.

Governance and social systems

Young's research has also extended into the modeling of governance and collective decision-making as networked, computational problems. His 2024 paper "Governance as a complex, networked, democratic, satisfiability problem," published in npj Complexity, frames democratic governance processes using the mathematical language of satisfiability problems, a concept borrowed from theoretical computer science, applied to networks of interacting agents and preferences.

Online behavior and social dynamics

Young has additionally examined the dynamics of online discourse. He is a co-author of "Correction: Impact and dynamics of hate and counter speech online," published in EPJ Data Science in 2023, a corrected version of research examining how hateful speech and counter-speech interact and evolve within online social platforms. His interests in social dynamics further extend to animal behavior research, as reflected in the 2023 paper "Opposing Responses to Scarcity Emerge from Functionally Unique Sociality Drivers," published in The American Naturalist, which examines how different mechanisms driving social behavior produce divergent responses to resource scarcity.

Recognition

Young's research has been supported by competitive federal funding, including two grants from the National Science Foundation totaling $162,648, administered through the University of Vermont and State Agricultural College. His publications have appeared in peer-reviewed venues including Physical Review E, the Proceedings of the Royal Society A, Royal Society Open Science, Communications Physics, npj Complexity, EPJ Data Science, and The American Naturalist, reflecting the interdisciplinary reach of his work across physics, mathematics, computer science, and biology. As of the most recent citation data, his body of work has accumulated 2,727 citations across 64 papers, corresponding to an h-index of 18.

Publications

• Young, J.G. et al. (2025). "One pathogen does not an epidemic make: a review of interacting contagions, diseases, beliefs, and stories." npj Complexity.
• Young, J.G. et al. (2025). "Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions." arXiv preprint.
• Young, J.G. et al. (2025). "Message passing for epidemiological interventions on networks with loops." arXiv preprint.
• Young, J.G. et al. (2024). "Governance as a complex, networked, democratic, satisfiability problem." npj Complexity.
• Young, J.G. et al. (2024). "Symmetry-driven embedding of networks in hyperbolic space." Communications Physics.
• Young, J.G. et al. (2024). "Reconstructing networks from simple and complex contagions." Physical Review E.
• Young, J.G. et al. (2023). "Exact and rapid linear clustering of networks with dynamic programming." Proceedings of the Royal Society A.
• Young, J.G. et al. (2023). "Correction: Impact and dynamics of hate and counter speech online." EPJ Data Science.
• Young, J.G. et al. (2023). "Accurately summarizing an outbreak using epidemiological models takes time." Royal Society Open Science.
• Young, J.G. et al. (2023). "Opposing Responses to Scarcity Emerge from Functionally Unique Sociality Drivers." The American Naturalist.

External links

• University of Vermont Faculty Profile
• Semantic Scholar Author Page
• National Science Foundation Award Search