Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

Naoki Masuda*, Prosenjit Kundu

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise.

本文言語English
論文番号023257
ジャーナルPhysical Review Research
4
2
DOI
出版ステータスPublished - 2022 6月
外部発表はい

ASJC Scopus subject areas

  • 物理学および天文学(全般)

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