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基于通信序列熵的复杂网络传输容量

马金龙 张俊峰 张冬雯 张红斌

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基于通信序列熵的复杂网络传输容量

马金龙, 张俊峰, 张冬雯, 张红斌

Quantifying complex network traffic capacity based on communicability sequence entropy

Ma Jin-Long, Zhang Jun-Feng, Zhang Dong-Wen, Zhang Hong-Bin
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  • 网络的传输性能在一定程度上依赖于网络的拓扑结构. 本文从结构信息的角度分析复杂网络的传输动力学行为, 寻找影响网络传输容量的信息结构测度指标. 通信序列熵可以有效地量化网络的整体结构信息, 为了表征网络整体传输能力, 把通信序列熵引入到复杂网络传输动力学分析中, 研究网络的通信序列熵与传输性能之间的关联特性, 分析这种相关性存在的内在机理. 分别在BA无标度和WS小世界网络模型上进行仿真, 结果显示: 网络的通信序列熵与其传输容量存在密切关联性, 随着通信序列熵的增加, 网络拓扑结构的均匀性随之增强, 传输容量明显增加. 网络的传输容量是通信序列熵的单调递增函数, 与通信序列熵成正关联关系. 通信序列熵可有效评估网络的传输容量, 本结论可为设计高传输容量网络提供理论依据.
    The transmission performance of the network depends to a certain extent on the topology of the network. This article analyzes the traffic dynamics of complex networks from the perspective of structural information, and looks for information structure measurement indicators that affect network traffic capacity. Existing research shows that the communicability sequence entropy of complex networks can effectively quantify the overall structure of the network. Based on this measurement, the difference between networks can be effectively quantified, and the connotation of sequence entropy of communicability can be explained. Communication sequence entropy can effectively quantify the overall structure information of the network. In order to characterize the overall traffic capacity of the network, the communication sequence entropy is introduced into the phenomenon of complex network congestion, the correlation between the network communication sequence entropy and the transmission performance is studied, and the internal mechanism of this correlation is analyzed. Simulations in BA scale-free network model and WS small-world network model show that the communication sequence entropy of the network is closely related to its traffic capacity. As the communication sequence entropy increases, the uniformity of the network topology will increase, and the traffic capacity will increase significantly. The traffic capacity of the network is a monotonically increasing function of the entropy of the communication sequence, and is positively correlated with the entropy of the communication sequence. The communication sequence entropy of the network can effectively evaluate the traffic capacity of the network. This conclusion can provide a theoretical basis for the design of a high traffic capacity network and help provide an effective strategy for the design of the high traffic capacity of the network, which can be optimized by increasing the communication sequence entropy.
      通信作者: 马金龙, mzjinlong@163.com
    • 基金项目: 2020年度河北省军民融合发展研究课题(批准号: HB20JMRH008)、教育部人文社科基金(批准号: 19YJAZH069)、国家自然科学基金(批准号: 61672206, 61572170)、河北省科技支撑计划(批准号: 18210109D, 20310701D)和河北省高层次人才资助项目(批准号: A2016002015)资助的课题
      Corresponding author: Ma Jin-Long, mzjinlong@163.com
    • Funds: Project supported by the 2020 Military-Civilian Integration Development Research Project of Hebei Province, China (Grant No. HB20JMRH008), the Humanities and Social Sciences Fund of the Ministry of Education of China (Grant No. 19YJAZH069), the National Natural Science Foundation of China (Grant Nos. 61672206, 61572170), the Science and Technology Support Program of Hebei Province, China (Grant Nos. 18210109D, 20310701D), and the High Level Talent Funding Project of Hebei Province, China (Grant No. A2016002015)
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  • 图 1  网络的通信序列熵SN与网络的度分布P(k)之间的关系图 (a) BA无标度网络; (b) WS小世界网络

    Fig. 1.  Rrelationship between the network’s communication sequence entropy SN and the network’s degree distribution P(k): (a) BA scale-free network; (b) WS small world network.

    图 2  (a)不同的通信序列熵的BA无标度网络下, 有序参数H(R)与数据包生成率R的关系; (b) BA无标度网络通信序列熵$S_{\rm N}$与传输容量$R_{\rm c}$的关系. 采用的路由策略为有效路由策略

    Fig. 2.  (a) Relationship between the order parameter H(R) and the packet generation rate R under BA scale-free network with different communication sequence entropy; (b) relationship between BA scale-free network communication sequence entropy$S_{\rm N}$ and traffic capacity $R_{\rm c}$. The routing strategy adopted is an effective routing strategy.

    图 3  (a)不同的通信序列熵的WS小世界网络下, 有序参数H(R)与数据包生成率R的关系; (b) WS小世界网络通信序列熵$S_{\rm N}$与传输容量$R_{\rm c}$的关系. 采用的路由策略为有效路由策略

    Fig. 3.  (a) Relationship between the order parameters $H(R)$and the packet generation rate R under the WS small world network with different communication sequence entropy; (b) relationship between WS small world network communication sequence entropy$S_{\rm N}$ and traffic capacity $R_{\rm c}$. The routing strategy adopted is an effective routing strategy.

    图 4  数据包负载 (traffic load) 在网络中节点度值上的分布情况 (a) BA无标度网络; (b) WS小世界网络

    Fig. 4.  Distribution of traffic load on degree value of nodes in the network: (a) BA scale-free network; (b) WS small world network.

    图 5  网络的通信序列熵$S_{\rm N}$与平均路径长度$\left\langle {L} \right\rangle $的关系 (a) BA无标度网络; (b) WS小世界网络

    Fig. 5.  Relationship between communication sequence entropy SN and average path length$\left\langle {L} \right\rangle $ in the network: (a) BA scale-free network; (b) WS small world network.

    图 6  网络的通信序列熵$S_{\rm N}$与节点的最大介数$B_{\rm {max}}$的关系 (a) BA无标度网络; (b) WS小世界网络

    Fig. 6.  Relationship between communication sequence entropy $S_{\rm N}$ and the maximum betweenness of nodes $B_{\rm {max}}$ in the network: (a) BA scale-free network; (b) WS small world network.

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    [2]

    Kumari S, Singh A 2019 Mod. Phys. Lett. B 1 13Google Scholar

    [3]

    Jiang L R, Jin X Y, Xia Y X, Ouyang B, Wu D P, Chen X 2014 Int. J. Distrib. Sens. Netw. 1155 764698Google Scholar

    [4]

    Tu H C, Xia Y X, Wu J J, Zhou X 2019 Physica A 9 17

    [5]

    Bianchin G, Pasqualetti F 2020 IEEE Trans. Intell. Transp. 3013 3024Google Scholar

    [6]

    Serok N, Levy O, Havlin S, Blumenfeld-Lieberthal E 2019 EPB: Urban Analytics and City Science 1362 1376Google Scholar

    [7]

    Li H J, Wang H, Chen L N 2014 Europhys. Lett. 108 68009Google Scholar

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    Xiang J, Wang Z Z, Li H J, Zhang Y, Li F, Dong L P, Li J M, Guo L J 2017 J. Stat. Mech. Theory Exp. 5 053213Google Scholar

    [9]

    Chen G, Kong R, Wang Y X 2020 Physica A 540 123002Google Scholar

    [10]

    Barabási A L, Albert R 1999 Science 509 512Google Scholar

    [11]

    Watts D J, Strogatz S H 1998 Nature 440 442

    [12]

    Tan F, Wu J J, Xia Y X, Tse C K 2014 Phys. Rev. E 89 062813Google Scholar

    [13]

    杨卓璇, 马源培, 李慧嘉 2020 聊城大学学报 (自然科学版) 12 26

    Yang Z X, Ma Y P, Li H J 2020 Journal of Liaocheng University (Social Science Edition) 12 26

    [14]

    马源培, 杨卓璇, 李慧嘉 2020 聊城大学学报(自然科学版) 26 32

    Ma Y P, Yang Z X, Li H J 2020 Journal of Liaocheng University (Social Science Edition) 26 32

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    Guimerà R, Díaz-Guilera A, Vega-Redondo F, Cabrales A, Arenas A 2002 Phys. Rev. Lett. 89 248701Google Scholar

    [16]

    Zhao L, Lai Y C, Park K, Ye N 2005 Phys. Rev. E 71 026125Google Scholar

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    Cai J, Wang Y, Liu Y, Luo J Z, Wei W G, Xu X P 2018 Future Gener. Comp. Syst. 765 771Google Scholar

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    蔡君, 余顺争 2013 物理学报 62 058901Google Scholar

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    [21]

    Chen R B, Cui W, Pu C L, Li J, Ji B, Gakis K, Pardalos P M 2018 Physica A 524 532Google Scholar

    [22]

    Cui J B, Xiang J, Liu Y, Hu K, Tang Y 2020 J. Phys. Soc. Jpn. 89 014802Google Scholar

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    DuBois T, Eubank S, Srinivasan A 2012 Electron. J. Comb. 1 19

    [24]

    Hu K, Liu C, Hu T, Tang Y 2010 J. Phys. A: Math. Theor. 43 175101Google Scholar

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出版历程
  • 收稿日期:  2020-08-10
  • 修回日期:  2020-10-09
  • 上网日期:  2021-03-17
  • 刊出日期:  2021-04-05

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