An Evolving Model Equivalent to BA Networks

ER random graph and BA networks play an important role in the networks science. When people investigate (networks,) ER random graph and its equivalent model are often used alternately. In this paper, we propose an evolving (model) equivalent to BA networks. We calculate analytically and simulate the degree distribution, clustering coefficient and (average) path length of the evolving model, which is identical with BA networks. In the evolution process of ours the global (knowledge) of the node degrees and preferential attachment are not necessary, so that the creation time of networks is (much shorter.) So when people investigate the properties of BA networks and their dynamics, our model may be used (interchangeably.)