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Inspired by studies on airline networks we propose a general model for weighted networks in which topological growth and weight dynamics are both determined by cost adversarial mechanism. Since transportation networks are designed and operated with objectives to reduce cost, the theory of cost in micro-economics plays a critical role in the evolution. We assume vertices and edges are given cost functions according to economics of scale and diseconomics of scale (congestion effect). With different cost functions the model produces broad distribution of networks. The model reproduces key properties of real airline networks: truncated degree distributions, nonlinear strength degree correlations, hierarchy structures, and particulary the disassortative and assortative behavior observed in different airline networks. The result suggests that the interplay between economics of scale and diseconomics of scale is a key ingredient in order to understand the underlying driving factor of the real-world weighted networks.
This paper presents an evolution model of weighted networks in which the structural growth and weight dynamics are driven by human behavior, i.e. passenger route choice behavior. Transportation networks grow due to peoples increasing travel demand an
From the macroscopic viewpoint for describing the acceleration behavior of drivers, this letter presents a weighted probabilistic cellular automaton model (the WP model, for short) by introducing a kind of random acceleration probabilistic distributi
We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is
We study the betweenness centrality of fractal and non-fractal scale-free network models as well as real networks. We show that the correlation between degree and betweenness centrality $C$ of nodes is much weaker in fractal network models compared t
Based on signaling process on complex networks, a method for identification community structure is proposed. For a network with $n$ nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the ini