7 papers
One pathogen does not an epidemic make: A review of interacting contagions, diseases, beliefs, and stories
Laurent Hébert-Dufresne, Yong-Yeol Ahn, Antoine Allard +15
From pathogens and computer viruses to genes and memes, contagion models have found widespread utility across the natural and social sciences. Despite their success and breadth of…
Message passing for epidemiological interventions on networks with loops
Erik Weis, Laurent Hébert-Dufresne, Jean-Gabriel Young
Spreading models capture key dynamics on networks, such as cascading failures in economic systems, (mis)information diffusion, and pathogen transmission. Here, we focus on design i…
Sensitivity analysis of epidemic forecasting and spreading on networks with probability generating functions
Mariah C. Boudreau, William H. W. Thompson, Christopher M. Danforth +2
Epidemic forecasting tools embrace the stochasticity and heterogeneity of disease spread to predict the growth and size of outbreaks. Conceptually, stochasticity and heterogeneity…
Symmetry-driven embedding of networks in hyperbolic space
Simon Lizotte, Jean-Gabriel Young, Antoine Allard
Hyperbolic models are known to produce networks with properties observed empirically in most network datasets, including heavy-tailed degree distribution, high clustering, and hier…
Governance as a complex, networked, democratic, satisfiability problem
Laurent Hébert-Dufresne, Nicholas W. Landry, Juniper Lovato +6
Democratic governments comprise a subset of a population whose goal is to produce coherent decisions, solving societal challenges while respecting the will of the people. New gover…
Network compression with configuration models and the minimum description length
Laurent Hébert-Dufresne, Jean-Gabriel Young, Alexander Daniels +2
Random network models, constrained to reproduce specific statistical features, are often used to represent and analyze network data and their mathematical descriptions. Chief among…