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The two-layer network model offers us a new viewpoint to observe the traffic dynamics of multilayer network systems. An efficient coupling mechanism is of great importance for alleviating the traffic congestion on two-layer networks. In order to reduce the network congestion and improve network transmission performance, the coupling mechanism between two layers of network and three coupling methods, which are random coupling, disassortative coupling and assortative coupling, are studied based on degree correlation. The packet transmission process is analyzed with both the shortest path routing strategy and degree-based weight routing strategy. The influences of the coupling mode and its corresponding routing strategy on the traffic capacity of the two-layer network are studied. In this paper, two scale-free networks are used to construct the two-layer network for simulation experiments. The network scale is in a range from 200 to 2400 with the value of average degree being 8. We focus on the traffic dynamics of two-layer network, and analyze the relationship between the traffic capacity and the three coupling modes, which are random coupling, disassortative coupling and assortative coupling, under the constraints of the shortest path routing strategy and the weight-based routing strategy. According to the characteristics of the coupling connection between the two layers of network, the best coupling method which is suitable for a certain routing strategy should be investigated. The suitable coupling connection between the two layers can effectively increase the traffic capacity. Both numerical result and analytical result show that the packet generation rate, average transmission time, and average throughput can be obviously improved under the shortest path routing strategy with the disassortative coupling method. When the degree-based static weight routing strategy is used, the traffic performance parameters such as packet generation rate, average transmission time, and average throughput can reach the optimal values with the assortative coupling method. It makes the traffic flow uniform that the routing strategy is chosen with the most suitable coupling method on the two-layer network, and the network traffic capacity may be effectively enhanced. More generally, the results indicate that the coupling modes can give rise to traffic behavior that relies subtly on the routing strategy on the two-layer network. Our work may shed some light on the design and optimization of some real traffic or communication networks.
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Keywords:
- coupling network /
- traffic capacity /
- coupling strength /
- average path length
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图 2 采用SPR策略AC, DC, RC 这三种耦合方式有序参数
$ \eta $ 与数据包产生率$ R $ 的关系 (a) BA-BA模型; (b) ER-ER模型; (c) SF-SF模型; (d) BA-ER模型; (e) BA-SF模型; (f) ER-SF模型Figure 2. Used the SPR strategy, the relationship between ordered parameters
$ \eta $ and packet generation rate$ R $ under the three coupling modes of AC, DC and RC: (a) BA-BA model; (b) ER-ER model; (c) SF-SF model; (d) BA-ER model; (e) BA-SF model; (f) ER-SF model图 3 采用DWR策略AC, DC, RC 这三种耦合方式
$ R $ 与控制参数$ \alpha $ 的关系 (a) BA-BA模型; (b) ER-ER模型; (c) SF-SF模型; (d) BA-ER模型; (e) BA-SF模型; (f) ER-SF模型Figure 3. Used the SPR strategy, the relationship between
$ R $ and control parameter$ \alpha $ under the three coupling modes of AC, DC and RC: (a) BA-BA model; (b) ER-ER model; (c) SF-SF model; (d) BA-ER model; (e) BA-SF model; (f) ER-SF model图 4 采用DWR策略AC, DC, RC 这三种耦合方式序参数
$ \eta $ 与数据包产生率$ R $ 的关系 (a) BA-BA模型; (b) ER-ER模型; (c) SF-SF模型; (d) BA-ER模型; (e) BA-SF模型; (f) ER-SF模型Figure 4. Used the DWR strategy, the relationship between ordered parameters
$ \eta $ and generation rate$ R $ under the three coupling modes of AC, DC and RC: (a) BA-BA model; (b) ER-ER model; (c) SF-SF model; (d) BA-ER model; (e) BA-SF model; (f) ER-SF model -
[1] Janaki T M, Gupte N 2003 Phy. Rev. E 67 021503
Google Scholar
[2] Albert R, Jeong H, Barabási A L 1999 Nature 401 130
Google Scholar
[3] 刘宏鲲, 周涛 2007 56 106
Google Scholar
Liu H K, Zhou T 2007 Acta Phys. Sin. 56 106
Google Scholar
[4] Ohira T, Sawatari R 1998 Phy. Rev. E 58 193
Google Scholar
[5] Solé R V, Valverde S 2001 Physica A 289 595
Google Scholar
[6] Guimerà R, Arenas A, Díaz G A, Giralt F 2002 Phy. Rev. E 66 026704
Google Scholar
[7] Woolf M, Arrowsmith D K, Mondragón C R J, Pitts J M 2002 Phy. Rev. E 66 046106
Google Scholar
[8] Arenas A, Díaz G A, Guimerà R 2001 Phys. Rev. Lett. 86 3196
Google Scholar
[9] Du W B, Wu Z X, Cai K Q 2013 Physica A 392 3505
Google Scholar
[10] 陈华良, 刘忠信, 陈增强, 袁著祉 2009 58 6068
Google Scholar
Chen H L, Liu Z X, Chen Z Q, Yuan Z Z 2009 Acta Phys. Sin. 58 6068
Google Scholar
[11] Zhou J, Yan G, Lai C H 2013 EPL-Europhys. Lett. 102 28002
Google Scholar
[12] Kurant M, Thiran P, Hagmann P 2007 Phys. Rev. E 76 026103
Google Scholar
[13] Du W B, Zhou X L, Chen Z, Cai K Q, Cao X B 2014 Chaos, Solitons Fractals 68 72
Google Scholar
[14] Tan F, Wu J J, Xia Y X, Tse C K 2014 Phys. Rev. E 89 062813
Google Scholar
[15] Kurant M, Thiran P 2006 Phys. Rev. Lett. 96 138701
Google Scholar
[16] Morris R G, Barthelemy M 2012 Phys. Rev. Lett. 109 128703
Google Scholar
[17] Chen S Y, Huang W, Cattani C, Altieri G 2012 Math. Prob. Eng. 2012 256
Google Scholar
[18] Fortz B, Thorup M 2002 IEEE J. Sel. Areas Commun. 20 756
Google Scholar
[19] Zhuo Y, Peng Y F, Yang X L, Long K 2011 Phys. Scr. 84 055802
Google Scholar
[20] 卓越 2011 计算机应用研究 28 3411
Google Scholar
Zhuo Y 2011 Appl. Res. Comput. 28 3411
Google Scholar
[21] Zhang S, Liang M G, Li H J 2014 Can. J. Phys. 92 1599
Google Scholar
[22] Zhang S, Liang M G, Jiang Z Y, Li Z Y 2015 Int. J. Mod. Phys. C 26 1550001
Google Scholar
[23] Ma J L, Han W Z, Guo Q, Zhang S, Wang J F, Wang Z H 2016 Int. J. Mod. Phys. C 27 1650044
Google Scholar
[24] Pu C L, Li S Y, Yang X X, Yang J, Wang K 2016 Physica A 447 261
Google Scholar
[25] Wang W, Tang M, Yang H, Do Y, Lai Y C, Lee G W 2014 Sci. Rep. 4 5097
Google Scholar
[26] Lee K M, Kim J Y, Cho W K, Goh K L, Kim I M 2012 New J. Phys. 14 033027
Google Scholar
[27] Cho W K, Min B, Goh K I 2010 Phys. Rev. E 81 066109
Google Scholar
[28] Gu C G, Zou S R, Xu X L, Qu Y Q, Jiang Y M, He D R, Liu H K, Zhou T 2011 Phy. Rev. E 84 026101
Google Scholar
[29] Wang K, Zhang Y F, Zhou S Y, Pei W J, Wang S P, Li T 2011 Physica A 390 2593
Google Scholar
[30] Zhuo Y, Peng Y F, Liu C, Liu Y K, Long K 2011 Physica A 390 2401
Google Scholar
[31] Yang S J 2005 Phy. Rev. E 71 016107
Google Scholar
[32] Zou S R, Zhou T, Liu A F, Xu X L, He D R 2010 Phys. Lett. A 374 4406
Google Scholar
[33] 王丹, 于灏, 井元伟, 姜囡, 张嗣瀛 2009 58 6802
Google Scholar
Wang D, Yu H, Jing Y W, Jiang N, Zhang S Y 2009 Acta Phys. Sin. 58 6802
Google Scholar
[34] 李涛, 裴文江, 王少平 2009 58 5903
Google Scholar
Li T, Pei W J, Wang S P 2009 Acta Phys. Sin. 58 5903
Google Scholar
[35] 濮存来, 裴文江 2010 59 3841
Google Scholar
Pu C L, Pei W J 2010 Acta Phys. Sin. 59 3841
Google Scholar
[36] Zhao L, Lai Y C, Park K, Ye N 2005 Phys. Rev. E 71 026125
Google Scholar
[37] Boccaletti S, Bianconi G, Criado R, Genio C L, Gómez G J, Romance M, Sendiña Nadal I, Wang Z, Zanin M 2014 Phys. Rep. 544 1
Google Scholar
[38] 刘伟彦, 刘斌 2014 63 248901
Google Scholar
Liu W Y, Liu B 2014 Acta Phys. Sin. 63 248901
Google Scholar
[39] 李世宝, 娄琳琳, 陈瑞祥, 洪利 2014 63 028901
Google Scholar
Li S B, Lou L L, Chen R X, Hong L 2014 Acta Phys. Sin. 63 028901
Google Scholar
[40] 杨先霞, 濮存来, 许忠奇, 陈荣斌, 吴洁鑫, 李伦波 2016 65 248901
Google Scholar
Yang X X, Pu C L, Xu Z Q, Chen R B, Wu J X, Li L B 2016 Acta Phys. Sin. 65 248901
Google Scholar
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