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A perturbation-based approach to identifying potentially superfluous network constituents.
- Source :
-
Chaos . Jun2023, Vol. 33 Issue 6, p1-15. 15p. - Publication Year :
- 2023
-
Abstract
- Constructing networks from empirical time-series data is often faced with the as yet unsolved issue of how to avoid potentially superfluous network constituents. Such constituents can result, e.g., from spatial and temporal oversampling of the system's dynamics, and neglecting them can lead to severe misinterpretations of network characteristics ranging from global to local scale. We derive a perturbation-based method to identify potentially superfluous network constituents that makes use of vertex and edge centrality concepts. We investigate the suitability of our approach through analyses of weighted small-world, scale-free, random, and complete networks. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SCALE-free network (Statistical physics)
*SYSTEM dynamics
*CENTRALITY
Subjects
Details
- Language :
- English
- ISSN :
- 10541500
- Volume :
- 33
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Chaos
- Publication Type :
- Academic Journal
- Accession number :
- 164704708
- Full Text :
- https://doi.org/10.1063/5.0152030