Abstract
While the use of networks to understand how complex systems respond to perturbations is pervasive across scientific disciplines, the uncertainty associated with estimates of pairwise interaction strengths (edge weights) remains rarely considered. Mischaracterizations of interaction strength can lead to qualitatively incorrect predictions regarding system responses as perturbations propagate through often counteracting direct and indirect effects. Here, we introduce PressPurt, a computational package for identifying the interactions whose strengths must be estimated most accurately in order to produce robust predictions of a network's response to press perturbations. The package provides methods for calculating and visualizing these edge-specific sensitivities (tolerances) when uncertainty is associated to one or more edges according to a variety of different error distributions. The software requires the network to be represented as a numerical (quantitative or qualitative) Jacobian matrix evaluated at stable equilibrium. PressPurt is open source under the MIT license and is available as both a Python package and an R package hosted at https://github.com/dkoslicki/PressPurt and on the CRAN repository https://CRAN.R-project.org/package=PressPurt.
Highlights
Networks have become a routine tool for representing the complex systems that pervade biology, technology and society
While the development of methods for inferring network topology remains a dominant focus,[18,19,21,22,23,24] numerous methods are being advanced for quantifying the weights of the edges between pairs of nodes in different network types.[1,5,7,9,10]
PressPurt thereby obviates the need for what are typically computationally expensive simulations whose results can be difficult to interpret when assessing the sources of mispredictions in complex networks
Summary
Networks have become a routine tool for representing the complex systems that pervade biology, technology and society. Predictive insight into perturbation effects remains hard to obtain This is true even when a network’s topology is fixed and precisely specified because edge weights are always subject to estimation error and empirical variation. We introduce PressPurt – a collection of computational tools designed to shed light on the qualitative and quantitative response of a network to press perturbations when there is uncertainty in the magnitude of edge weights. PressPurt is designed to identify the most sensitive interactions within the network which must be estimated most accurately to produce robust predictions of press perturbation responses
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