  A general scheme for detecting and analyzing topological patterns in large
complex networks is presented. In this scheme the network in question is
compared with its properly randomized version that preserves some of its
low-level topological properties. Statistically significant deviation of any
measurable property of a network from this null model likely reflect its design
principles and/or evolutionary history. We illustrate this basic scheme on the
example of the correlation profile of the Internet quantifying correlations
between connectivities of its neighboring nodes. This profile distinguishes the
Internet from previously studied molecular networks with a similar scale-free
connectivity distribution. We finally demonstrate that clustering in a network
is very sensitive to both the connectivity distribution and its correlation
profile and compare the clustering in the Internet to the appropriate null
model.

Statistical Mechanics;Disordered Systems and Neural Networks