节点动态增长对复杂网络传输效率的影响研究
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摘要
在真实的复杂网络中,网络节点会因为网络拓扑结构的变化而增减,进而导致网络节点间传输效率降低.针对这一问题,通过分析复杂网络节点的动态变化,提出网络节点增加的动态传输模型,并利用真实复杂网络的数据模拟仿真,研究网络节点变化对网络传输效率的影响.结果表明:网络的初始大小会随网络节点的动态增加而变化,其传输效率受节点动态增加的影响在最初阶段表现明显,随着节点的继续增加,网络传输效率会趋于平稳,表现出稳定的网络特性.在这个过程中,复杂网络每次新加入节点的个数和节点边维持了网络信息传输的信息量,强化了网络传的输性能,使得网络具有较好的总体控制能力和有效的节点连接方式.
In real complex network, the nodes will increace with the structure of network topology and if take no account of the change of node numbers, the transfer efficiency among nodes will reduce. Aiming at the problem, this paper put forward dynamic transfer model.Using the data simulation of real network to analysis the influence of node change to transfer efficiency. The result indicates that the network capacity will change over with the increase of node numbers. At the early stage, the influence varies significantly and will verge to smooth and steady subsequently.In the whole process, the new increased nodes maintain the information content and strenghth the network transfer performance, which lead to a better overall control ability and effective connection type.
In real complex network, the nodes will increace with the structure of network topology and if take no account of the change of node numbers, the transfer efficiency among nodes will reduce. Aiming at the problem, this paper put forward dynamic transfer model.Using the data simulation of real network to analysis the influence of node change to transfer efficiency. The result indicates that the network capacity will change over with the increase of node numbers. At the early stage, the influence varies significantly and will verge to smooth and steady subsequently.In the whole process, the new increased nodes maintain the information content and strenghth the network transfer performance, which lead to a better overall control ability and effective connection type.
引文
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[2]Barabasi A L,Albert R.Emergence of scaling in random networks[J].Science,1999,286(5439):509-512.
[3]Newman M E J,Watts D J,Strogatz S H.Random graph models of social networks[C]//Proceedings of the National Academy of Sciences of the United States of America,2002,99Suppl 1:2566-2572.
[4]RékaAlbert,Albert-LászlóBarabási.Statistical mechanics of complex networks[J].Reviews of Modern Physics,2002,74(1):47-97.
[5]刘刚,李永树.交通流局部单向传输对复杂网络拥塞的影响[J].科学通报,2013,58(4):305-312.
[6]张古鹏.小世界创新网络动态演化及其效应研究[J].管理科学学报,2015,06:15-29.
[7]魏永松,李少远.带有网络拓扑优化的分布式预测控制方法[J].控制理论与应用,2015(9):1269-1276.
[8]Takehara,Takuma,Ochiai,Fumio,Suzuki,Naoto.A small-world network model of facial emotion recognition[J].Quarterly Journal of Experimental Psychology,2006,69(8):1508-29.
[9]田立新,贺莹环,黄益.一种新型二分网络类局域世界演化模型[J].物理学报,2012,22:558-564.
[10]丁琳,张嗣瀛.面向级联失效的复杂网络加权策略[J].控制与决策,2013,28(9):1399-1402.
[11]郭晓永,李俊民.一种新的复杂动态网络学习控制的自适应同步算法[J].控制理论与应用,2012,29(8).
[12]杨月全,余星火,张天平.时变内耦合复杂网络的平衡态同步smart变结构控制[J].控制理论与应用,2010,27(2):181-187.
[13]杨康,张仲义.基于复杂网络理论的供应链网络风险传播机理研究[J].系统科学与数学,2013(10):1224-1232.
[14]贺向南.随机复杂网络的同步性分析,应用及数据处理的若干问题[D].复旦大学,2013.
[15]任晓龙,吕琳媛.网络重要节点排序方法综述[J].科学通报,2014(13):1175-1197.16.
[16]范文礼,刘志刚.基于传输效率矩阵的复杂网络节点重要度排序方法[J].西南交通大学学报,2014(02):337-342.