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Scale-free behavior of networks with the copresence of preferential and uniform attachment rules. / Pachon, Angelica; Sacerdote, Laura; Yang, Shuyi.

In: Physica D: Nonlinear Phenomena, Vol. 371, 15.05.2018, p. 1-12.

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Pachon, Angelica ; Sacerdote, Laura ; Yang, Shuyi. / Scale-free behavior of networks with the copresence of preferential and uniform attachment rules. In: Physica D: Nonlinear Phenomena. 2018 ; Vol. 371. pp. 1-12.

BibTeX

@article{2fadf55d54754bdb9f84233d8405a7ff,
title = "Scale-free behavior of networks with the copresence of preferential and uniform attachment rules",
abstract = "Complex networks in different areas exhibit degree distributions with a heavy upper tail. A preferential attachment mechanism in a growth process produces a graph with this feature. We herein investigate a variant of the simple preferential attachment model, whose modifications are interesting for two main reasons: to analyze more realistic models and to study the robustness of the scale-free behavior of the degree distribution. We introduce and study a model which takes into account two different attachment rules: a preferential attachment mechanism (with probability 1−p) that stresses the rich get richer system, and a uniform choice (with probability p) for the most recent nodes, i.e. the nodes belonging to a window of size w to the left of the last born node. The latter highlights a trend to select one of the last added nodes when no information is available. The recent nodes can be either a given fixed number or a proportion (αn) of the total number of existing nodes. In the first case, we prove that this model exhibits an asymptotically power-law degree distribution. The same result is then illustrated through simulations in the second case. When the window of recent nodes has a constant size, we herein prove that the presence of the uniform rule delays the starting time from which the asymptotic regime starts to hold. The mean number of nodes of degree k and the asymptotic degree distribution are also determined analytically. Finally, a sensitivity analysis on the parameters of the model is performed.",
keywords = "Barab{\'a}si–Albert random graph, Degree distribution, Preferential and uniform attachments",
author = "Angelica Pachon and Laura Sacerdote and Shuyi Yang",
year = "2018",
month = "5",
day = "15",
doi = "10.1016/j.physd.2018.01.005",
language = "English",
volume = "371",
pages = "1--12",
journal = "Physica D: Nonlinear Phenomena",
issn = "0167-2789",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Scale-free behavior of networks with the copresence of preferential and uniform attachment rules

AU - Pachon, Angelica

AU - Sacerdote, Laura

AU - Yang, Shuyi

PY - 2018/5/15

Y1 - 2018/5/15

N2 - Complex networks in different areas exhibit degree distributions with a heavy upper tail. A preferential attachment mechanism in a growth process produces a graph with this feature. We herein investigate a variant of the simple preferential attachment model, whose modifications are interesting for two main reasons: to analyze more realistic models and to study the robustness of the scale-free behavior of the degree distribution. We introduce and study a model which takes into account two different attachment rules: a preferential attachment mechanism (with probability 1−p) that stresses the rich get richer system, and a uniform choice (with probability p) for the most recent nodes, i.e. the nodes belonging to a window of size w to the left of the last born node. The latter highlights a trend to select one of the last added nodes when no information is available. The recent nodes can be either a given fixed number or a proportion (αn) of the total number of existing nodes. In the first case, we prove that this model exhibits an asymptotically power-law degree distribution. The same result is then illustrated through simulations in the second case. When the window of recent nodes has a constant size, we herein prove that the presence of the uniform rule delays the starting time from which the asymptotic regime starts to hold. The mean number of nodes of degree k and the asymptotic degree distribution are also determined analytically. Finally, a sensitivity analysis on the parameters of the model is performed.

AB - Complex networks in different areas exhibit degree distributions with a heavy upper tail. A preferential attachment mechanism in a growth process produces a graph with this feature. We herein investigate a variant of the simple preferential attachment model, whose modifications are interesting for two main reasons: to analyze more realistic models and to study the robustness of the scale-free behavior of the degree distribution. We introduce and study a model which takes into account two different attachment rules: a preferential attachment mechanism (with probability 1−p) that stresses the rich get richer system, and a uniform choice (with probability p) for the most recent nodes, i.e. the nodes belonging to a window of size w to the left of the last born node. The latter highlights a trend to select one of the last added nodes when no information is available. The recent nodes can be either a given fixed number or a proportion (αn) of the total number of existing nodes. In the first case, we prove that this model exhibits an asymptotically power-law degree distribution. The same result is then illustrated through simulations in the second case. When the window of recent nodes has a constant size, we herein prove that the presence of the uniform rule delays the starting time from which the asymptotic regime starts to hold. The mean number of nodes of degree k and the asymptotic degree distribution are also determined analytically. Finally, a sensitivity analysis on the parameters of the model is performed.

KW - Barabási–Albert random graph

KW - Degree distribution

KW - Preferential and uniform attachments

U2 - 10.1016/j.physd.2018.01.005

DO - 10.1016/j.physd.2018.01.005

M3 - Article

AN - SCOPUS:85041319034

VL - 371

SP - 1

EP - 12

JO - Physica D: Nonlinear Phenomena

JF - Physica D: Nonlinear Phenomena

SN - 0167-2789

ER -

ID: 2840788