Name
Abstract | ||
|---|---|---|
Assessing whether a given network is typical or atypical for a random-network ensemble (i.e., network-ensemble comparison) 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 Erdos-Renyi 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1137/17M1124218 | SIAM JOURNAL ON APPLIED MATHEMATICS |
Keywords | DocType | Volume |
network science,von Neumann entropy,network-ensemble comparison,network rewiring,null models,mean field theory | Journal | 78 |
Issue | ISSN | Citations |
2 | 0036-1399 | 1 |
PageRank | References | Authors |
0.36 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zichao Li | 1 | 1 | 1.71 |
| Peter J. Mucha | 2 | 909 | 64.78 |
| Dane Taylor | 3 | 74 | 7.19 |