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Acceleratingly growing network model with widely weighted dynamics

穆军芬;周进

  Many real systems possess accelerating statistics where the total number of edges grows faster than the number of nodes and the growth is nonlinear.In this paper,we present a novel weighted network model with accelerating growth.Moreover,in contrast with previous models where weights are assigned statically or rearranged locally,we allow the flows to be widely updated.This model gives power-law distributions of degree,weight and strength,as confirmed in many real networks.Particularly,the exponents are nonuniversal and depend on two network parameters.And it shows that the droop-head and heavy-tail properties of these distributions,which are observed in many real-world networks,can be reflected by this new network model.It turns out that the strength highly correlates with the degree and displays scale-free property,which is in consistence with empirical evidence.Simulations are provided to demonstrate the theoretical results.……