Structural Identification of Large Stochastic Network Based on Random Graphs with Non-Linear Preferential Attaching Rule
Random graphs generated by a non-linear preferential attaching rule (NRPA) graphs) are proposed for modeling of stochastic networks such as the Internet. A method for the exact solution for degree distribution of vertices in NRPA with the given parameters of the generator graphs is developed. Calibration technique for generators of the NRPA graphs with the known vertices degree distribution is offered.