Random inner product graphs form a special case of inhomogeneous random graphs. 
The model outline is that vertices are generated from a distribution $\mu$ in $d$-dimensional  space, 
and two vertices are connected with an edge with probability proportional 
to the inner product of their corresponding vectors. 
We show that, under the strong inner product condition, 
random inner product graphs with minimum degree $\Omega ({\log}^2 n)$ 
have constant conductance, with high probability.
However: (1) Their conductance depends on $\mu$ and may differ from classical random graphs. 
(2)Their “sparsest cuts” may involve sets of vertices of cardinalities 
much larger than the cardinalities of the sparsest cuts of classical random graphs. 
The above is in accordance with measurements in many real complex networks.