Networks in life: Scaling properties and eigenvalue spectra

I. Farkas; I. Derényi; H. Jeong; Z. Néda; Z. N. Oltvai; E. Ravasz; A. Schubert; A. L. Barabási; T. Vicsek

doi:10.1016/S0378-4371(02)01181-0

Networks in life: Scaling properties and eigenvalue spectra

I. Farkas, I. Derényi, H. Jeong, Z. Néda, Z. N. Oltvai, E. Ravasz, A. Schubert, A. L. Barabási, T. Vicsek

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71 Scopus citations

Abstract

We analyze growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells. For the explicit demonstration of the scale-free nature and hierarchical organization of these graphs, a deterministic construction is also used. We demonstrate the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.

Original language	English (US)
Pages (from-to)	25-34
Number of pages	10
Journal	Physica A: Statistical Mechanics and its Applications
Volume	314
Issue number	1-4
DOIs	https://doi.org/10.1016/S0378-4371(02)01181-0
State	Published - 2002
Externally published	Yes

Keywords

Collaboration graphs
Graph spectra
Random networks
Spectral analysis of real-world graphs

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T1 - Networks in life

T2 - Scaling properties and eigenvalue spectra

AU - Farkas, I.

AU - Derényi, I.

AU - Jeong, H.

AU - Néda, Z.

AU - Oltvai, Z. N.

AU - Ravasz, E.

AU - Schubert, A.

AU - Barabási, A. L.

AU - Vicsek, T.

PY - 2002

Y1 - 2002

N2 - We analyze growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells. For the explicit demonstration of the scale-free nature and hierarchical organization of these graphs, a deterministic construction is also used. We demonstrate the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.

AB - We analyze growing networks ranging from collaboration graphs of scientists to the network of similarities defined among the various transcriptional profiles of living cells. For the explicit demonstration of the scale-free nature and hierarchical organization of these graphs, a deterministic construction is also used. We demonstrate the use of determining the eigenvalue spectra of sparse random graph models for the categorization of small measured networks.

KW - Collaboration graphs

KW - Graph spectra

KW - Random networks

KW - Spectral analysis of real-world graphs

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JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

IS - 1-4

ER -

Networks in life: Scaling properties and eigenvalue spectra

Abstract

Keywords

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