Delivering capacity allocation strategy for traffic dynamics on scale-free networks

Jinlong Ma, Wei Sui (), Changfeng Du (), Xiangyang Xu () and Guanghua Zhang ()
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Jinlong Ma: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Wei Sui: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Changfeng Du: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Xiangyang Xu: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Guanghua Zhang: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 02, 1-10

Abstract: The traffic dynamics of complex networks are largely determined by the node’s resource distribution. In this paper, based on the shortest path routing strategy, a node delivering capacity distribution mechanism is proposed into the traffic dynamics in Barabási and Albert (BA) scale-free networks; the efficiency of the mechanism on the network capacity measured by the critical point (Rc) of phase transition from free flow to congestion is primarily explored. Based on the proposed strategy, the total delivering capacity is reallocated according to both degree and betweenness of each node, and an optimal value of parameter αc is found, leading to the maximum traffic capacity. The results of numerical experiments on scale-free networks suggest that the resource allocation strategy proposed here is capable of effectively enhancing the transmission capacity of networks. Furthermore, this study may provide novel insights into research on networked traffic systems.

Keywords: Scale-free networks; traffic capacity; betweenness centrality; delivering capacity (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120500291

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