Work

Network-Ensemble Comparisons with Stochastic Rewiring and Von Neumann Entropy

Year: 2018

Type: article

Abstract: ) has widespread applications ranging from null-model selection and hypothesis testing to clustering and classifying networks. We develop a framework for network-ensemble comparison by subjecting the network to stochastic rewiring. We study two rewiring processes-uniform and degree-preserved rewiring-which yield random-network ensembles that converge to the Erdős-Rényi and configuration-model ensembles, respectively. We study convergence through von Neumann entropy (VNE)-a network summary statistic measuring information content based on the spectra of a Laplacian matrix-and develop a perturbation analysis for the expected effect of rewiring on VNE. Our analysis yields an estimate for how many rewires are required for a given network to resemble a typical network from an ensemble, offering a computationally efficient quantity for network-ensemble comparison that does not require simulation of the corresponding rewiring process.

Source: SIAM Journal on Applied Mathematics

DOI: 10.1137/17m1124218

Authors: Zichao Li, Peter J. Mucha, Dane Taylor

Institutions: University of North Carolina at Chapel Hill, University at Buffalo, State University of New York

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: James S. McDonnell Foundation, National Institutes of Health, Eunice Kennedy Shriver National Institute of Child Health and Human Development

Awards: Community Detection in Networks Across Time, Models and Tools for Dynamic Health-Relevant Diffusion over Complex Networks