Abstract
Network evolution is a key issue of network study to understand
design principle underlying network organization . We investigate
the evolution mechanisms of weighted networks by analyzing
time-dependent data of real weighted networks. We find power-law
growth rates of networks and identify generalized kernel functions
which drive network evolution. Based on observed results, we
propose an evolving weighted network model. Our model successfully
reproduce strength, degree and weight distributions of real
networks. In addition, we study weight-topology correlations in
model and real weighted networks.
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