Properties of asymmetrical evolving networks

被引:5
作者
Zheng, Jian-Feng [1 ]
Gao, Zi-You
Zhao, Hui
机构
[1] Beijing Jiaotong Univ, Inst Syst Sci, Sch Traffic & Transportat, Beijing 100044, Peoples R China
[2] Beijing Jiaotong Univ, State Key Lab Rail Traffic Control & Safety, Beijing 100044, Peoples R China
基金
中国国家自然科学基金;
关键词
complex networks; scale-free networks; exponential networks;
D O I
10.1016/j.physa.2006.10.065
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Introducing utility to describe the attractions of nodes in this work, we propose and study a simple asymmetrical evolving network model, considering both preferential attachment and randoin (controlled by probability 1)). That is, the utility increment Delta u(j) not equal Delta u(j) when connecting node i to node j. The simulation results show that the model can reproduce power-law distributions of utility P(u) similar to mu(-a), a = 2 + 1/p, which can be obtained using mean field approximation. Furthermore, the model exhibits exponential networks with respect to small values of p and power-law scale-free networks with respect to big values of p, which is in good agreement with theoretical analysis. In other words, the model represents a transition between exponential and power-law scaling. To better understand the degree correlations of our model, the clustering coefficient C and degree assortativity r depending on probability p are also discussed. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码:719 / 724
页数:6
相关论文
共 50 条
[21]   Evolving weighted scale-free networks [J].
Dorogovtsev, SN ;
Mendes, JFF .
SCIENCE OF COMPLEX NETWORKS: FROM BIOLOGY TO THE INTERNET AND WWW, 2005, 776 :29-36
[22]   Evolving dynamical networks with transient cluster activity [J].
Maslennikov, Oleg V. ;
Nekorkin, Vladimir I. .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2015, 23 (1-3) :10-16
[23]   Kinetics of node splitting in evolving complex networks [J].
Colman, E. R. ;
Rodgers, G. J. .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2012, 391 (24) :6626-6631
[24]   Link prediction in evolving heterogeneous networks using the NARX neural networks [J].
Ozcan, Alper ;
Oguducu, Sule Gunduz .
KNOWLEDGE AND INFORMATION SYSTEMS, 2018, 55 (02) :333-360
[25]   Hybrid evolving clique-networks and their communicability [J].
Ding, Yimin ;
Zhou, Bin ;
Chen, Xiaosong .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2014, 407 :198-203
[26]   Asymptotic Mandelbrot Law for Some Evolving Networks [J].
Jiyuan Tan Li Li Yi Zhang Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China .
Tsinghua Science and Technology, 2012, 17 (03) :310-312
[27]   Growth signals determine the topology of evolving networks [J].
Vranic, Ana ;
Dankulov, Marija Mitrovic .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2021, 2021 (01)
[28]   A general evolving model for growing bipartite networks [J].
Tian, Lixin ;
He, Yinghuan ;
Liu, Haijun ;
Du, Ruijin .
PHYSICS LETTERS A, 2012, 376 (23) :1827-1832
[29]   Asymptotic Mandelbrot law for some evolving networks [J].
Li, L. (li-li@mail.tsinghua.edu.cn), 1600, Tsinghua University Press (17)
[30]   Evolving activity cascades on socio-technological networks [J].
Borge-Holthoefer J. ;
Piedrahita P. ;
Arenas A. .
Journal of Computational Social Science, 2018, 1 (1) :67-79