Random Birth-and-Death Networks

Random Birth-and-Death Networks
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随机生死网络

DOI:
10.1007/s10955-016-1447-6
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发表时间:
2015-03
影响因子:
1.6
通讯作者:
Rayman-Bacchus, Lez
Rayman-Bacchus, Lez
中科院分区:
物理与天体物理3区
文献类型:
--
作者:
Zhang, Xiaojun;He, Zheng;Rayman-Bacchus, Lez

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本文考虑了一个基本模型-随机生灭网络(RBDN)模型,在该模型中,在每一时间步,一个新节点以概率p()加入网络并均匀连接到旧节点,或者一个已有节点以概率p()从网络中删除。该模型允许网络规模的波动,反映了包括物理学、生态学和经济学在内的许多不同学科的网络行为。本研究的目的是发展RBDN模型,并探讨其基本的统计特性。针对不同的情况,首先讨论了RBDN的网络规模,然后结合基于随机过程规则的马尔可夫链方法和概率母函数方法,给出了度分布的精确解。最后,通过仿真验证,探讨了度分布的尾部特征。我们的研究结果表明,RBDN的度分布的尾部在的情况下呈现泊松尾,而在的情况下呈现指数尾。
In this paper, a baseline model termed as random birth-and-death network (RBDN) model is considered, in which at each time step, a new node is added into the network with probabilityp() and connected tomold nodes uniformly, or an existing node is deleted from the network with probability. This model allows for fluctuations in size, reflecting the behaviour of networks in many different disciplines including physics, ecology and economics. The purpose of this study is to develop the RBDN model and explore its basic statistical properties. For differentp, we first discuss the network size of RBDN, then combining the stochastic process rules based Markov chain method and the probability generating function method, we provide the exact solutions of the degree distributions. Finally, the tail characteristics of the degree distributions are explored after simulation verification. Our results show that the tail of the degree distribution for RBDN exhibits a Poisson tail in the case ofand an exponential tail aspapproaches to 1.
DOI: 10.1103/physreve.66.026704
发表时间: 2002-08-01
期刊: PHYSICAL REVIEW E
影响因子: 2.4
作者:
Guimerà, R;Arenas, A;Giralt, F
通讯作者: Giralt, F
DOI: 10.1103/physreve.69.026101
发表时间: 2003-03
期刊: Physical review. E, Statistical, nonlinear, and soft matter physics
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发表时间: 2008-05
期刊: Physica D: Nonlinear Phenomena
影响因子: --
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