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Register a new userA weighted network evolution model based on passenger behavior
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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 and the pattern of growth is determined by their route choice behavior. In airline networks passengers often transfer from a third airport instead of flying directly to the destination, which contributes to the hubs formation and finally the scale-free statistical property. In this model we assume at each time step there emerges a new node with m travel destinations. Then the new node either connects destination directly with the probability p or transfers from a third node with the probability 1-p. The analytical result shows degree and strength both obey power-law distribution with the exponent between 2.33 and 3 depending on p. The weights also obey power-law distribution. The clustering coefficient, degree assortatively coefficient and degree-strength correlation are all dependent on the probability p. This model can also be used in social networks.
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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.
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