Evolving Network Modeling Driven by the Degree Increase and Decrease Mechanism

被引:7
|
作者
Li, Yuhan [1 ]
Feng, Minyu [1 ]
Kurths, Juergen [2 ,3 ]
机构
[1] Southwest Univ, Coll Artificial Intelligence, Chongqing 400715, Peoples R China
[2] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany
[3] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2023年 / 53卷 / 09期
关键词
Adaptation models; Analytical models; Statistics; Sociology; Servers; Markov processes; Adaptive systems; Degree distribution; evolving network; network modeling; queueing system; BIRTH; CHAINS;
D O I
10.1109/TSMC.2023.3268372
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Ever since the Barabasi-Albert (BA) scale-free network has been proposed, network modeling has been studied intensively in light of the network growth and the preferential attachment (PA). However, numerous real systems are featured with a dynamic evolution including network reduction in addition to network growth. In this article, we propose a novel mechanism for evolving networks from the perspective of vertex degree. We construct a queueing system to describe the increase and decrease of vertex degree, which drives the network evolution. In our mechanism, the degree increase rate is regarded as a function positively correlated to the degree of a vertex, ensuring the PA in a new way. Degree distributions are investigated under two expressions of the degree increase rate, one of which manifests a "long tail", and another one varies with different values of parameters. In simulations, we compare our theoretical distributions with simulation results and also apply them to real networks, which presents the validity and applicability of our model.
引用
收藏
页码:5369 / 5380
页数:12
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