Analytical and numerical methods of calibration for preferential attachment randon graphs

Methods of random graphs growing with nonlinear preferential attachment rule, which have the required distributions of vertices and edges attachment degrees are developed. Application of these methods allows us to generate random graphs, which adequately simulate growing stochastic networks - social, information, transport and many others. Examples of application for the developed methods are given.