Structural transitions in scale-free networks

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Structural transitions in scale-free networks. / Szabo, G.; Alava, Mikko J.; Kertesz, J.

In: Physical Review E, Vol. 67, No. 5, 056102, 2003, p. 1-5.

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Szabo, G. ; Alava, Mikko J. ; Kertesz, J. / Structural transitions in scale-free networks. In: Physical Review E. 2003 ; Vol. 67, No. 5. pp. 1-5.

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@article{071f16a823984d67bc8aa1a84ca3330b,
title = "Structural transitions in scale-free networks",
abstract = "Real growing networks such as the World Wide Web or personal connection based networks are characterized by a high degree of clustering, in addition to the small-world property and the absence of a characteristic scale. Appropriate modifications of the (Barab{\'a}si-Albert) preferential attachment network growth capture all these aspects. We present a scaling theory to describe the behavior of the generalized models and the mean-field rate equation for clustering. This is solved for a specific case with the result C(k)∼1/k for the clustering of a node of degree k. This mean-field exponent agrees with simulations, and reproduces the clustering of many real networks.",
keywords = "power-laws, scale-free networks, power-laws, scale-free networks, power-laws, scale-free networks",
author = "G. Szabo and Alava, {Mikko J.} and J. Kertesz",
year = "2003",
doi = "10.1103/PhysRevE.67.056102",
language = "English",
volume = "67",
pages = "1--5",
journal = "Physical Review E",
issn = "2470-0045",
publisher = "American Physical Society",
number = "5",

}

RIS - Download

TY - JOUR

T1 - Structural transitions in scale-free networks

AU - Szabo, G.

AU - Alava, Mikko J.

AU - Kertesz, J.

PY - 2003

Y1 - 2003

N2 - Real growing networks such as the World Wide Web or personal connection based networks are characterized by a high degree of clustering, in addition to the small-world property and the absence of a characteristic scale. Appropriate modifications of the (Barabási-Albert) preferential attachment network growth capture all these aspects. We present a scaling theory to describe the behavior of the generalized models and the mean-field rate equation for clustering. This is solved for a specific case with the result C(k)∼1/k for the clustering of a node of degree k. This mean-field exponent agrees with simulations, and reproduces the clustering of many real networks.

AB - Real growing networks such as the World Wide Web or personal connection based networks are characterized by a high degree of clustering, in addition to the small-world property and the absence of a characteristic scale. Appropriate modifications of the (Barabási-Albert) preferential attachment network growth capture all these aspects. We present a scaling theory to describe the behavior of the generalized models and the mean-field rate equation for clustering. This is solved for a specific case with the result C(k)∼1/k for the clustering of a node of degree k. This mean-field exponent agrees with simulations, and reproduces the clustering of many real networks.

KW - power-laws

KW - scale-free networks

KW - power-laws

KW - scale-free networks

KW - power-laws

KW - scale-free networks

U2 - 10.1103/PhysRevE.67.056102

DO - 10.1103/PhysRevE.67.056102

M3 - Article

VL - 67

SP - 1

EP - 5

JO - Physical Review E

JF - Physical Review E

SN - 2470-0045

IS - 5

M1 - 056102

ER -

ID: 3569524