1. Eigenvalue spectra and stability of directed complex networks
- Author
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Joseph W. Baron, Agencia Estatal de Investigación (España), Ministerio de Ciencia e Innovación (España), European Commission, and Baron, Joseph W.
- Subjects
Physics ,FOS: Physical sciences ,Disordered Systems and Neural Networks (cond-mat.dis-nn) ,Disordered Systems and Neural Networks ,Condensed Matter - Disordered Systems and Neural Networks - Abstract
11 pages, 7 figures, Quantifying the eigenvalue spectra of large random matrices allows one to understand the factors that contribute to the stability of dynamical systems with many interacting components. This work explores the effect that the interaction network between components has on the eigenvalue spectrum. We build on previous results, which usually only take into account the mean degree of the network, by allowing for nontrivial network degree heterogeneity. We derive closed-form expressions for the eigenvalue spectrum of the adjacency matrix of a general weighted and directed network. Using these results, which are valid for any large well-connected complex network, we then derive compact formulas for the corrections (due to nonzero network heterogeneity) to well-known results in random matrix theory. Specifically, we derive modified versions of the Wigner semicircle law, the Girko circle law, and the elliptic law and any outlier eigenvalues. We also derive a surprisingly neat analytical expression for the eigenvalue density of a directed Barabási-Albert network. We are thus able to deduce that network heterogeneity is mostly a destabilizing influence in complex dynamical systems., The author acknowledges partial financial support from the Agencia Estatal de Investigación (AEI, MCI, Spain) and Fondo Europeo de Desarrollo Regional (FEDER, UE), under Project PACSS (RTI2018-093732-B-C21) and the Maria de Maeztu Program for units of Excellence in R&D, Grant MDM-2017-0711 funded by MCIN/AEI/10.13039/501100011033.
- Published
- 2022