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On weightd scale-free network model with tunable clustering and congesstion

Wang Dan Jin Xiao-Zheng

On weightd scale-free network model with tunable clustering and congesstion

Wang Dan, Jin Xiao-Zheng
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  • We propose an evolutionary model for weighted network with tunable clustering coefficient according to characteristics of real network. The model gives power-law distributions of degree, weight, and strength, as confirmed in many real network. In particular, the weighted model has a nonlinear correlation between average clustering coefficient and degree, which is in good agreement with flat head real weighted technological network. Moreove, the effect of the weighted network structure on traffic delivery is studied. The packet traffic flow on the weighted scale-free network is investigated based on the local routing strategy using node strength, and the delivering ability of node is controlled by node strength. The simulations show that the traffic dynamics depends strongly on the controlled parameter.
    • Funds: Project support by the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 61203152, 61104029), and the Scientific Research Foundation for Doctor of Liaoning Province of China (Grant No. 20121040).
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    Erdös P, Rényi A 1959 Publ. Math. 6 290

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    Watts D J, Strogatz S H 1998 Nature 393 440

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    Barabási A L, Albert R 1999 Science 286 509

    [8]

    Li J, Wang B H, Jiang P Q, Zhou T, Wang W X 2006 Acta Phys. Sin. 55 4051 (in Chinese) [李季, 汪秉宏, 蒋品群, 周涛, 王文旭2006物理学报 55 4051]

    [9]

    Yakubo K, KoroSak D 2011 Phys. Rev. E 83 066111

    [10]

    Ferretti L, Cortelezzi M 2011 Phys. Rev. E 84 016103

    [11]

    Gao J X, Chen Z, Cai Y Z, Xu X M 2010 Phys. Rev. E 81 041918

    [12]

    Chakraborty A, Manna S S 2010 Phys. Rev. E 81 016111

    [13]

    Baronchelli A, Castellano C, Pastor-Satorras R 2011 Phys. Rev. E 83 066117

    [14]

    Geng X M, Wen G H 2007 Int. J. Mod. Phys. C 18 1435

    [15]

    Wen G H, Duan Z S, Chen G R, Geng X M 2011 Physica A 390 4012

    [16]

    Barrat A, Barthélemy M, Vespignani A 2004 Phys. Rev. Lett. 92 228701

    [17]

    Xie Z, Li X, Wang X F 2008 Comm. Theor. Phys. 50 261

    [18]

    Kagawa Y, Takamatsu A 2009Phys. Rev. E 79 046216

    [19]

    Fagiolo G, Reyes J, Schiavo S 2009 Phys. Rev. E 79 036115

    [20]

    Garlaschelli D, Loffredo M I 2009 Phys. Rev. Lett. 102 038701

    [21]

    Furuya S, Yakubo K 2008 Phys. Rev. E 78 066104

    [22]

    Holme P, Kim B J 2002 Phys. Rev. E 65 026107

    [23]

    Krapivsky P L, Redner S, Leyvraz F 2000 Phys. Rev. Lett. 85 4629

    [24]

    Albert R, Jeong H, Barabai A L 1999 Nature 401 130

    [25]

    Ravasz E, Barabasi A L 2003 Phys. Rev. E 67 026112

    [26]

    Wang W X, Wang B H, Hu B, Yan G, Ou Q 2005 Phys. Rev. Lett. 94 188702

    [27]

    Wang D, Yu H, Jing Y W, Jing N, Zhang S Y 2009 Acta Phys. Sin. 58 6802 (in Chinese) [王丹, 于灏, 井元伟, 姜囡, 张嗣瀛 2009 物理学报 58 6802]

  • [1]

    Faloutsos M, Faloutsos P, Faloutsos C 1999 Comp. Comm. Rev. 29 251

    [2]

    Redner S 1998 Eur. Phys. J. B 4 131

    [3]

    Barabasi A L, Jeong H, Neda Z, Ravasz E, Schubert A, Vicsek T 2002 Physica A 311 590

    [4]

    Jeong H, Mason S P, Barabasi A L, Oltvai Z N 2001 Nature 411 41

    [5]

    Erdös P, Rényi A 1959 Publ. Math. 6 290

    [6]

    Watts D J, Strogatz S H 1998 Nature 393 440

    [7]

    Barabási A L, Albert R 1999 Science 286 509

    [8]

    Li J, Wang B H, Jiang P Q, Zhou T, Wang W X 2006 Acta Phys. Sin. 55 4051 (in Chinese) [李季, 汪秉宏, 蒋品群, 周涛, 王文旭2006物理学报 55 4051]

    [9]

    Yakubo K, KoroSak D 2011 Phys. Rev. E 83 066111

    [10]

    Ferretti L, Cortelezzi M 2011 Phys. Rev. E 84 016103

    [11]

    Gao J X, Chen Z, Cai Y Z, Xu X M 2010 Phys. Rev. E 81 041918

    [12]

    Chakraborty A, Manna S S 2010 Phys. Rev. E 81 016111

    [13]

    Baronchelli A, Castellano C, Pastor-Satorras R 2011 Phys. Rev. E 83 066117

    [14]

    Geng X M, Wen G H 2007 Int. J. Mod. Phys. C 18 1435

    [15]

    Wen G H, Duan Z S, Chen G R, Geng X M 2011 Physica A 390 4012

    [16]

    Barrat A, Barthélemy M, Vespignani A 2004 Phys. Rev. Lett. 92 228701

    [17]

    Xie Z, Li X, Wang X F 2008 Comm. Theor. Phys. 50 261

    [18]

    Kagawa Y, Takamatsu A 2009Phys. Rev. E 79 046216

    [19]

    Fagiolo G, Reyes J, Schiavo S 2009 Phys. Rev. E 79 036115

    [20]

    Garlaschelli D, Loffredo M I 2009 Phys. Rev. Lett. 102 038701

    [21]

    Furuya S, Yakubo K 2008 Phys. Rev. E 78 066104

    [22]

    Holme P, Kim B J 2002 Phys. Rev. E 65 026107

    [23]

    Krapivsky P L, Redner S, Leyvraz F 2000 Phys. Rev. Lett. 85 4629

    [24]

    Albert R, Jeong H, Barabai A L 1999 Nature 401 130

    [25]

    Ravasz E, Barabasi A L 2003 Phys. Rev. E 67 026112

    [26]

    Wang W X, Wang B H, Hu B, Yan G, Ou Q 2005 Phys. Rev. Lett. 94 188702

    [27]

    Wang D, Yu H, Jing Y W, Jing N, Zhang S Y 2009 Acta Phys. Sin. 58 6802 (in Chinese) [王丹, 于灏, 井元伟, 姜囡, 张嗣瀛 2009 物理学报 58 6802]

  • [1] Wang Dan, Hao Bin-Bin. A weighted scale-free network model with high clustering and its synchronizability. Acta Physica Sinica, 2013, 62(22): 220506. doi: 10.7498/aps.62.220506
    [2] Pan Zao-Feng, Wang Xiao-Fan. A weighted scale-free network model with large-scale tunable clustering. Acta Physica Sinica, 2006, 55(8): 4058-4064. doi: 10.7498/aps.55.4058
    [3] Wang Dan, Yu Hao, Jing Yuan-Wei, Jiang Nan, Zhang Si-Ying. Study on the congestion in complex network based on traffic awareness algorithm. Acta Physica Sinica, 2009, 58(10): 6802-6808. doi: 10.7498/aps.58.6802
    [4] Liu Gang, Li Yong-Shu. Study on the congestion phenomena in complex network based on gravity constraint. Acta Physica Sinica, 2012, 61(10): 108901. doi: 10.7498/aps.61.108901
    [5] Wang Dan, Jing Yuan-Wei, Hao Bin-Bin. Extended Holme-Kim network model and synchronizability. Acta Physica Sinica, 2012, 61(22): 220511. doi: 10.7498/aps.61.220511
    [6] Luan Ling, Li Yan, Lü Ling. Lag synchronization of spatiotemporal chaos in a weighted network with ring connection. Acta Physica Sinica, 2009, 58(7): 4463-4468. doi: 10.7498/aps.58.4463
    [7] Di Zeng-Ru, Tian Liu, Yao Hong. Effect of distribution of weight on the efficiency of weighted networks. Acta Physica Sinica, 2011, 60(2): 028901. doi: 10.7498/aps.60.028901
    [8] Lü Ling, Meng Le, Guo Li, Zou Jia-Rui, Yang Ming. Projective synchronization of a weighted network in a laser spatiotemporal chaos model. Acta Physica Sinica, 2011, 60(3): 030506. doi: 10.7498/aps.60.030506
    [9] Shen Yi, Xu Huan-Liang. The evaluation function of weight similarity and its application in community detection in weighted networks. Acta Physica Sinica, 2010, 59(9): 6022-6028. doi: 10.7498/aps.59.6022
    [10] Dai Cun-Li, Wu Wei, Zhao Yan-Yan, Yao Xue-Xia, Zhao Zhi-Gang. Effect of weight distribution on the synchronization of weighted generalized local-world networks. Acta Physica Sinica, 2013, 62(10): 108903. doi: 10.7498/aps.62.108903
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  • Received Date:  06 January 2012
  • Accepted Date:  04 June 2012
  • Published Online:  20 November 2012

On weightd scale-free network model with tunable clustering and congesstion

  • 1. Key Laboratory of Manufacturing Industrial Integrated Automation, Shenyang University, Shenyang 110044, China
Fund Project:  Project support by the Young Scientists Fund of the National Natural Science Foundation of China (Grant Nos. 61203152, 61104029), and the Scientific Research Foundation for Doctor of Liaoning Province of China (Grant No. 20121040).

Abstract: We propose an evolutionary model for weighted network with tunable clustering coefficient according to characteristics of real network. The model gives power-law distributions of degree, weight, and strength, as confirmed in many real network. In particular, the weighted model has a nonlinear correlation between average clustering coefficient and degree, which is in good agreement with flat head real weighted technological network. Moreove, the effect of the weighted network structure on traffic delivery is studied. The packet traffic flow on the weighted scale-free network is investigated based on the local routing strategy using node strength, and the delivering ability of node is controlled by node strength. The simulations show that the traffic dynamics depends strongly on the controlled parameter.

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