The Analysis Of Generation And The Structure Of The Network Based On Continuously Preferential Attachment

With economic globalization and rapid development of science and techniques, close cooperation among nations, industries, and persons are essential for various products and industrial developments, like spaceflight, high-speed rail, HGP(Human Genome Project) and so on. However, how goes this cooperation between different industries—main industries seek first-class sub-industries and then sub-industries select another well-done ones, and consequently a huge network of socially preferential attachment comes into being. It is very significant for cooperation between industries to research how evolve networks and then what properties networks formed have. On the bases of background above, the paper extends the BA scale-free network model, considering the effect of the second and third preferential attachment after the first preferential one on network structure.The first section overviews the significance of researching generating network models, mathematical properties reflecting network topological structure, and classic generating network models, then introduces the research results of this paper.The second section, based on BA network evolving model, takes into account the second preferential attachment of the node the first one preferentially attach according to the demand of this attached node. Furthermore, the degree distribution of the model is obtained by applying mean-field method. Finally, it shows that the generated network still conforms with the scale-free property through comparison of stochastic and numerical simulation, and the power exponents generate large influence on the degree distribution of network.The third section applies identical mean-field method same as the second section but considers the three continuous preferential attachment, then calculates the degree distribution; whatever, comparison of stochastic and numerical simulation confirms the theoretical analysis and indicates the scope of power exponent can be enlarged, which enhance our insight into real network.