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Found 9 papers, 6 papers with code
Universal nonlinear infection kernel from heterogeneous exposure on higher-order networks
The colocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network.
Physics and Society Adaptation and Self-Organizing Systems
Network comparison and the within-ensemble graph distance
Quantifying the differences between networks is a challenging and ever-present problem in network science.
Physics and Society Social and Information Networks
Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network
Mathematical disease modelling has long operated under the assumption that any one infectious disease is caused by one transmissible pathogen spreading among a population.
Detecting structural perturbations from time series with deep learning
Small disturbances can trigger functional breakdowns in complex systems.
Social confinement and mesoscopic localization of epidemics on networks
Recommendations around epidemics tend to focus on individual behaviors, with much less efforts attempting to guide event cancellations and other collective behaviors since most models lack the higher-order structure necessary to describe large gatherings.
Physics and Society Adaptation and Self-Organizing Systems
Recovering the past states of growing trees
In principle one can reconstruct the past states of a growing network from only its current state.
Social and Information Networks Physics and Society
Efficient sampling of spreading processes on complex networks using a composition and rejection algorithm
Efficient stochastic simulation algorithms are of paramount importance to the study of spreading phenomena on complex networks.
Physics and Society Social and Information Networks
Universality of the stochastic block model
Mesoscopic pattern extraction (MPE) is the problem of finding a partition of the nodes of a complex network that maximizes some objective function.
Phase transition in the recoverability of network history
Network growth processes can be understood as generative models of the structure and history of complex networks.
