Models of random graphs, for real computer networks I am interested in models of random graphs which are similar to the graphs of real computer networks. I am not sure if the common well-studied $G(n,p)$ model ($n$ vertices, each possible edge is selected with probability $p$) is good for studying real computer networks (is it?).


  what models of random graphs are useful for understanding computer networks araising in practice?


More generally, what other models of finite random graphs (other than those equivalent to the $G(n,p)$ model) have been studied in the literature? (An ideal answer would be a pointer to a survey for studied models of finite random graphs.)
This survey, The structure and function of complex networks by Newman, reviews techniques and models for real complex networks including concepts such as small-world effect, degree distributions, and random graph models. Also, the same author has a nice paper, Random graphs as models of networks, about adaptations of random graphs to model real networks.

References:

1) Random graphs as models of networks, M. E. J. Newman, in Handbook of Graphs and Networks, S. Bornholdt and H. G. Schuster (eds.), Wiley-VCH, Berlin (2003)

2) The structure and function of complex networks, M. E. J. Newman, SIAM Review 45, 167-256 (2003)