109 citations · 477 across the 19 of their papers we have counts for
19 papers
Adaptive hypergraphs and the characteristic scale of higher-order contagions using generalized approximate master equations
Giulio Burgio, Guillaume St-Onge, Laurent Hébert-Dufresne
People organize in groups and contagions spread across them. A simple process, but complex to model due to dynamical correlations within groups and between groups. Groups can also…
Nonlinear bias toward complex contagion in uncertain transmission settings
Guillaume St-Onge, Laurent Hébert-Dufresne, Antoine Allard
Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of trans…
Hierarchical team structure and multidimensional localization (or siloing) on networks
Laurent Hébert-Dufresne, Guillaume St-Onge, John Meluso +2
Knowledge silos emerge when structural properties of organizational interaction networks limit the diffusion of information. These structural barriers are known to take many forms…
Source-sink cooperation dynamics constrain institutional evolution in a group-structured society
Laurent Hébert-Dufresne, Timothy M. Waring, Guillaume St-Onge +6
Societies change through time, entailing changes in behaviors and institutions. We ask how social change occurs when behaviors and institutions are interdependent. We model a group…
Influential groups for seeding and sustaining nonlinear contagion in heterogeneous hypergraphs
Guillaume St-Onge, Iacopo Iacopini, Vito Latora +4
Several biological and social contagion phenomena, such as superspreading events or social reinforcement, are the results of multi-body interactions, for which hypergraphs offer a…
Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks
Guillaume St-Onge, Hanlin Sun, Antoine Allard +2
The colocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to captu…
Network comparison and the within-ensemble graph distance
Harrison Hartle, Brennan Klein, Stefan McCabe +4
Quantifying the differences between networks is a challenging and ever-present problem in network science. In recent years a multitude of diverse, ad hoc solutions to this problem…
Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network
Blake J. M. Williams, Guillaume St-Onge, Laurent Hébert-Dufresne
Mathematical disease modelling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen spreading among a population. This par…
Detecting structural perturbations from time series with deep learning
Edward Laurence, Charles Murphy, Guillaume St-Onge +2
Small disturbances can trigger functional breakdowns in complex systems. A challenging task is to infer the structural cause of a disturbance in a networked system, soon enough to…
Threefold way to the dimension reduction of dynamics on networks: an application to synchronization
Vincent Thibeault, Guillaume St-Onge, Louis J. Dubé +1
Several complex systems can be modeled as large networks in which the state of the nodes continuously evolves through interactions among neighboring nodes, forming a high-dimension…
Master equation analysis of mesoscopic localization in contagion dynamics on higher-order networks
Guillaume St-Onge, Vincent Thibeault, Antoine Allard +2
Simple models of infectious diseases tend to assume random mixing of individuals, but real interactions are not random pairwise encounters: they occur within various types of gathe…
Social confinement and mesoscopic localization of epidemics on networks
Guillaume St-Onge, Vincent Thibeault, Antoine Allard +2
Recommendations around epidemics tend to focus on individual behaviors, with much less efforts attempting to guide event cancellations and other collective behaviors since most mod…
Inference for growing trees
George T. Cantwell, Guillaume St-Onge, Jean-Gabriel Young
One can often make inferences about a growing network from its current state alone. For example, it is generally possible to determine how a network changed over time or pick among…
Thresholding normally distributed data creates complex networks
George T. Cantwell, Yanchen Liu, Benjamin F. Maier +4
Network data sets are often constructed by some kind of thresholding procedure. The resulting networks frequently possess properties such as heavy-tailed degree distributions, clus…
Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm
Guillaume St-Onge, Jean-Gabriel Young, Laurent Hébert-Dufresne +1
Efficient stochastic simulation algorithms are of paramount importance to the study of spreading phenomena on complex networks. Using insights and analytical results from network s…
Universality of the stochastic block model
Jean-Gabriel Young, Guillaume St-Onge, Patrick Desrosiers +1
Mesoscopic pattern extraction (MPE) is the problem of finding a partition of the nodes of a complex network that maximizes some objective function. Many well-known network inferenc…
Geometric evolution of complex networks
Charles Murphy, Antoine Allard, Edward Laurence +2
We present a general class of geometric network growth mechanisms by homogeneous attachment in which the links created at a given time are distributed homogeneously between a n…
Phase transition of the susceptible-infected-susceptible dynamics on time-varying configuration model networks
Guillaume St-Onge, Jean-Gabriel Young, Edward Laurence +2
We present a degree-based theoretical framework to study the susceptible-infected-susceptible (SIS) dynamics on time-varying (rewired) configuration model networks. Using this fram…
Susceptible-infected-susceptible dynamics on the rewired configuration model
Guillaume St-Onge, Jean-Gabriel Young, Edward Laurence +2
We investigate the susceptible-infected-susceptible dynamics on configuration model networks. In an effort for the unification of current approaches, we consider a network whose ed…