activity
20172025
most citedDeep learning of contagion dynamics on complex networks

83 citations · 170 across the 8 of their papers we have counts for

collaborators

8 papers

stat.AP2025

On the reconstruction limits of complex networks

Charles Murphy, Ariane Lizotte, François Thibault +3

Network reconstruction consists in retrieving the hidden interaction structure of a system from observations. Many reconstruction algorithms have been proposed, although less resea…

cond-mat.stat-mech2022★ 8 cited

Duality between predictability and reconstructability in complex systems

Charles Murphy, Vincent Thibeault, Antoine Allard +1

Predicting the evolution of a large system of units using its structure of interaction is a fundamental problem in complex system theory. And so is the problem of reconstructing th…

physics.soc-ph2020★ 45 cited

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-ph2020★ 2 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…

physics.soc-ph2020★ 83 cited

Deep learning of contagion dynamics on complex networks

Charles Murphy, Edward Laurence, Antoine Allard

Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tracta…

physics.soc-ph2017★ 6 cited

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…

physics.soc-ph2017★ 24 cited

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…

physics.soc-ph2017★ 2 cited

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…