Localization of Laplacian eigenvectors on random networks
Abstract In large random networks, each eigenvector of the Laplacian matrix tends to localize on a subset of network nodes having similar numbers of edges, namely, the components of each Laplacian eigenvector take relatively large values only on a particular subset of nodes whose degrees are close....
Saved in:
Main Authors: | Shigefumi Hata, Hiroya Nakao |
---|---|
Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2017
|
Subjects: | |
Online Access: | https://doaj.org/article/3b61cac4c4914fafa2a343c00f7453b7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Eigenvector Centrality is a Metric of Elastomer Modulus, Heterogeneity, and Damage
by: P. M. Welch, et al.
Published: (2017) -
Eigenvector-spatial localisation
by: Travis Harty, et al.
Published: (2021) -
Laplacian eigenfunctions learn population structure.
by: Jun Zhang, et al.
Published: (2009) -
Node and edge nonlinear eigenvector centrality for hypergraphs
by: Francesco Tudisco, et al.
Published: (2021) -
Toward emulating nuclear reactions using eigenvector continuation
by: C. Drischler, et al.
Published: (2021)