A Research on Complex Network Models Based on Poisson Growth

The mechanisms of Growth and Preferential Attachment of BA model simply demonstrate the process of scale-free network's formation. Based on the facts that nodes in real networks arrive in batches, and growth rate of edges in networks is larger than that of nodes, this thesis utilizes the concept that nodes in queue theory arrive in Poisson process, coming up with a new complex network model. Nodes in this model grow in a non-linear way as a Poisson process with rate lambda(1); and edges grow in a non-linear way as a Poisson process with rate lambda(2). Employing mean field theory, this thesis concludes that degree distribution of networks obeys power-law distribution, and BA scale-free network model is a particular case of this model.