A novel evolving scale-free model with tunable attractiveness

doi:10.1088/1674-1056/19/7/070204

中国物理B ›› 2010, Vol. 19 ›› Issue (7): 70204-070204.doi: 10.1088/1674-1056/19/7/070204

A novel evolving scale-free model with tunable attractiveness

王昊¹, 刘绚², 刘天琪², 李兴源²

(1)College of Electronics and Information Engineering, Sichuan University, Chengdu 610000, China; (2)School of Electrical Engineering and Automation, Sichuan University, Chengdu 610000, China

修回日期:2010-01-18 出版日期:2010-07-15 发布日期:2010-07-15
基金资助:
Project supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2008BAA13B01).

A novel evolving scale-free model with tunable attractiveness

Liu Xuan (刘绚)^a, Liu Tian-Qi (刘天琪)^a, Wang Hao (王昊)^b, Li Xing-Yuan (李兴源)^a

^a School of Electrical Engineering and Automation, Sichuan University, Chengdu 610000, China; ^b College of Electronics and Information Engineering, Sichuan University, Chengdu 610000, China

Revised:2010-01-18 Online:2010-07-15 Published:2010-07-15
Supported by:
Project supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2008BAA13B01).

摘要/Abstract

摘要： In this paper, a new evolving model with tunable attractiveness is presented. Based on the Barabasi—Albert (BA) model, we introduce the attractiveness of node which can change with node degree. Using the mean-field theory, we obtain the analytical expression of power-law degree distribution with the exponent γ ∈ (3,∞). The new model is more homogeneous and has a lower clustering coefficient and bigger average path length than the BA model.

Abstract: In this paper, a new evolving model with tunable attractiveness is presented. Based on the Barabasi—Albert (BA) model, we introduce the attractiveness of node which can change with node degree. Using the mean-field theory, we obtain the analytical expression of power-law degree distribution with the exponent $\gamma \in (3,\infty)$. The new model is more homogeneous and has a lower clustering coefficient and bigger average path length than the BA model.

Key words: scale-free, tunable attractiveness, degree distribution, clustering coefficient

中图分类号: (Networks and genealogical trees)

89.75.Hc

02.30.Jr (Partial differential equations) 02.50.Cw (Probability theory)

刘绚, 刘天琪, 王昊, 李兴源. A novel evolving scale-free model with tunable attractiveness[J]. 中国物理B, 2010, 19(7): 70204-070204.

Liu Xuan (刘绚), Liu Tian-Qi (刘天琪), Wang Hao (王昊), Li Xing-Yuan (李兴源). A novel evolving scale-free model with tunable attractiveness[J]. Chin. Phys. B, 2010, 19(7): 70204-070204.

参考文献 20

[1]	Kinney R, Crucitti P, Albert R and Latora V 2005 Europ. Phys. J. B 46 101
[2]	Vázquez A, Pastor-Satorras R and Vespignani A 2002 Phys. Rev. E 65 066130
[3]	Newman M E J 2001 Proc. Natl. Acad. Sci. USA 98 404
[4]	Albert R, Jeong H and Barabási A L 1999 Nature 401 130
[5]	Barabási A L and Albert R 1999 Science 286 509
[6]	Newman M E J 2003 SIAM Rev. 45 167
[7]	Bianconi G and Barabási A L 2001 Phys. Rev. Lett. 86 5632
[8]	Ergün G and Rodgers G J 2002 Physica A 303 261
[9]	Huang Z X, Wang X R and Zhu H 2004 Chin. Phys. 13 273
[10]	Chen C 2007 Physica A 377 709
[11]	Dorogovtsev S N, Mendes J F F and Samukhin A N 2000 Phys. Rev. Lett. 85 4633
[12]	Vázquez A 2003 Phys. Rev. E 67 056104
[13]	Simas T and Rocha L M 2008 Phys. Rev. E 78 066116
[14]	Dorogovtsev S N and Mendes J F F 2000 Phys. Rev. E 62 1842
[15]	Chen Fei, Chen Z Q and Yuan Z Z 2007 Chin. Phys. 16 287
[16]	Shao Z G, Zou X W, Tan Z J and Jin Z Z 2006 J. Phys. A: Math. Gen 39 2035
[17]	Albert R and Barabási A L 2002 Rev. Mod. Phys. bf74 47
[18]	Dorogovtsev S N and Mendes J F F 2000 Phys. Rev. E 62 1842
[19]	Tao S H, Yang C, Li H N and Zhang Y 2009 Computer Engineering 35 03
[20]	Newman M E J, Moore C and Watts D J 2000 Phys. Rev. Lett. 84 3201

A novel evolving scale-free model with tunable attractiveness

A novel evolving scale-free model with tunable attractiveness

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献 20

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Fuzhong Nian(年福忠) and Xia Zhang(张霞). Topological phase transition in network spreading[J]. 中国物理B, 2023, 32(3): 38901-038901.
[2]	Yijun Ran(冉义军), Tianyu Liu(刘天宇), Tao Jia(贾韬), and Xiao-Ke Xu(许小可). A novel similarity measure for mining missing links in long-path networks[J]. 中国物理B, 2022, 31(6): 68902-068902.
[3]	Dan-Dan Zhao(赵丹丹), Wang-Xin Peng(彭王鑫), Hao Peng(彭浩), and Wei Wang(王伟). Effects of heterogeneous adoption thresholds on contact-limited social contagions[J]. 中国物理B, 2022, 31(6): 68906-068906.
[4]	Dong-Jie Qian(钱冬杰). Explosive synchronization in a mobile network in the presence of a positive feedback mechanism[J]. 中国物理B, 2022, 31(1): 10503-010503.
[5]	Yu-Wei Yan(严玉为), Yuan Jiang(蒋沅), Rong-Bin Yu(余荣斌), Song-Qing Yang(杨松青), and Cheng Hong(洪成). Cascading failures of overload behaviors using a new coupled network model between edges[J]. 中国物理B, 2022, 31(1): 18901-018901.
[6]	Fuzhong Nian(年福忠), Jingzhou Li(李经洲), and Xin Guo(郭鑫). Evolution mechanism of Weibo top news competition[J]. 中国物理B, 2021, 30(12): 128901-128901.
[7]	Dongli Duan(段东立), Tao Chai(柴涛), Xixi Wu(武茜茜), Chengxing Wu(吴成星), Shubin Si(司书宾), and Genqing Bian(边根庆). Identification of unstable individuals in dynamic networks[J]. 中国物理B, 2021, 30(9): 90501-090501.
[8]	Tong-Feng Weng(翁同峰), Xin-Xin Cao(曹欣欣), and Hui-Jie Yang(杨会杰). Complex network perspective on modelling chaotic systems via machine learning[J]. 中国物理B, 2021, 30(6): 60506-060506.
[9]	Zi-Wei Yuan(袁紫薇), Chang-Chun Lv(吕长春), Shu-Bin Si(司书宾), and Dong-Li Duan(段东立). Dynamical robustness of networks based on betweenness against multi-node attack[J]. 中国物理B, 2021, 30(5): 50501-050501.
[10]	Xiao-Long Peng(彭小龙) and Yi-Dan Zhang(张译丹). Contagion dynamics on adaptive multiplex networks with awareness-dependent rewiring[J]. 中国物理B, 2021, 30(5): 58901-058901.
[11]	. [J]. 中国物理B, 2021, 30(4): 48901-.
[12]	. [J]. 中国物理B, 2020, 29(12): 128901-.
[13]	. [J]. 中国物理B, 2020, 29(12): 128902-.
[14]	苏桂锋, 李晓温, 张小兵, 张一. Finite density scaling laws of condensation phase transition in zero-range processes on scale-free networks[J]. 中国物理B, 2020, 29(8): 88904-088904.
[15]	闫小丽, 崔亚鹏, 倪顺江. Identifying influential spreaders in complex networks based on entropy weight method and gravity law[J]. 中国物理B, 2020, 29(4): 48902-048902.