Network extreme eigenvalue: From mutimodal to scale-free networks

DOI10.1063/1.3697990zbMath1331.94096arXiv1107.2473OpenAlexW3105136944WikidataQ51391732 ScholiaQ51391732MaRDI QIDQ2787738

No author found.

Publication date: 4 March 2016

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Abstract: The extreme eigenvalues of adjacency matrices are important indicators on the influences of topological structures to collective dynamical behavior of complex networks. Recent findings on the ensemble averageability of the extreme eigenvalue further authenticate its sensibility in the study of network dynamics. Here we determine the ensemble average of the extreme eigenvalue and characterize the deviation across the ensemble through the discrete form of random scale-free network. Remarkably, the analytical approximation derived from the discrete form shows significant improvement over the previous results. This has also led us to the same conclusion as [Phys. Rev. Lett. 98, 248701 (2007)] that deviation in the reduced extreme eigenvalues vanishes as the network size grows.

Full work available at URL: https://arxiv.org/abs/1107.2473

Mathematics Subject Classification ID

Circuits, networks (94C99)

Cites Work

• The onset of synchronization in systems of globally coupled chaotic and periodic oscillators
• Connected components in random graphs with given expected degree sequences
• From the Cover: The structure of scientific collaboration networks
• Statistical mechanics of complex networks
• Emergence of Scaling in Random Networks
• Synchronization in networks with random interactions: Theory and applications
• The Many Proofs and Applications of Perron's Theorem
• A critical point for random graphs with a given degree sequence
• Spectra of random graphs with given expected degrees

This page was built for publication: Network extreme eigenvalue: From mutimodal to scale-free networks