activity
20182023
most citedDetecting structural perturbations from time series with deep learning

2 citations · 4 across the 3 of their papers we have counts for

collaborators
Showing physics.soc-phShow all

10 papers · 1 filter

physics.soc-ph20232 cited

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…

physics.soc-ph2022

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…

physics.soc-ph2021

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…

physics.soc-ph2021

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…

physics.soc-ph2020

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…

physics.soc-ph20202 cited

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…