General Dynamics of Topology and Traffic on Weighted Technological Networks

Wang, Wen-Xu; Wang, Bing–Hong; Hu, Bo; Yan, Gang; Ou, Qing

doi:10.1103/physrevlett.94.188702

Cited by 249 publications

(129 citation statements)

References 20 publications

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“…The dynamic model in Ref. [10] brings in a strength parameter, and the power-law distribution has γ ∈ (2,3]. Although it can provide a larger interval for γ by adjusting the strength parameter, it is a tedious job to assign a local strength to each edge.…”

Section: Scale-free Network: Instruction and Constructionmentioning

confidence: 99%

Convergence speed of consensus problems over undirected scale-free networks

Sun¹,

Dou²

2010

Chinese Phys. B

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Scale-free networks and consensus behaviour among multiple agents have both attracted much attention. To investigate the consensus speed over scale-free networks is the major topic of the present work. A novel method is developed to construct scale-free networks due to their remarkable power-law degree distributions, while preserving the diversity of network topologies. The time cost or iterations for networks to reach a certain level of consensus is discussed, considering the influence from power-law parameters. They are both demonstrated to be reversed power-law functions of the algebraic connectivity, which is viewed as a measurement on convergence speed of the consensus behaviour. The attempts of tuning power-law parameters may speed up the consensus procedure, but it could also make the network less robust over time delay at the same time. Large scale of simulations are supportive to the conclusions.

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Section: Scale-free Network: Instruction and Constructionmentioning

confidence: 99%

Convergence speed of consensus problems over undirected scale-free networks

Sun¹,

Dou²

2010

Chinese Phys. B

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“…Most previous weighted random models [31,32,33,34,35] with topology and weight coevolution, however, possess very loose clustering structures when the size of the networks is large. At the same time, previous deterministic models, are mainly unweighted [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], which ignore the heterogeneity of edges in real networks.…”

Section: Introductionmentioning

confidence: 99%

Deterministic weighted scale-free small-world networks

Zhang

Zhou

et al. 2010

Physica A: Statistical Mechanics and its Applications

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We propose a deterministic weighted scale-free small-world model for considering pseudofractal web with the coevolution of topology and weight. Considering the fluctuations in traffic flow constitute a main reason for congestion of packet delivery and poor performance of communication networks, we suggest a recursive algorithm to generate the network, which restricts the traffic fluctuations on it effectively during the evolutionary process. we provide a relatively complete view of topological structure and weight dynamics characteristics of the networks: weight and strength distribution; degree correlations; average clustering coefficient and degree-cluster correlations; as well as the diameter.

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“…To better mimic the reality, Barrat, Barthélemy, and Vespignani presented a growing model (BBV) for weighted networks, where the evolutions of degree and weight are coupled [15,16]. Enlightened by BBV's remarkable work, a variety of models and mechanisms for weighted networks have been proposed, including weight-driven model [17], traffic-driven evolution models [18,19], fitness models [20], local-world models [21,22,23], deterministic models [24,25,26], weight-dependent deactivation [27], spatial constraints [28,29].…”

Section: Introductionmentioning

confidence: 99%

Effects of accelerating growth on the evolution of weighted complex networks

Zhang

Fang

Zhou

et al. 2009

Physica A: Statistical Mechanics and its Applications

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Many real systems possess accelerating statistics where the total number of edges grows faster than the network size. In this paper, we propose a simple weighted network model with accelerating growth. We derive analytical expressions for the evolutions and distributions for strength, degree, and weight, which are relevant to accelerating growth. We also find that accelerating growth determines the clustering coefficient of the networks. Interestingly, the distributions for strength, degree, and weight display a transition from scale-free to exponential form when the parameter with respect to accelerating growth increases from a small to large value. All the theoretical predictions are successfully contrasted with numerical simulations.

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General Dynamics of Topology and Traffic on Weighted Technological Networks

Cited by 249 publications

References 20 publications

Convergence speed of consensus problems over undirected scale-free networks

Convergence speed of consensus problems over undirected scale-free networks

Deterministic weighted scale-free small-world networks

Effects of accelerating growth on the evolution of weighted complex networks

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