  The social networks that infectious diseases spread along are typically
clustered. Because of the close relation between percolation and epidemic
spread, the behavior of percolation in such networks gives insight into
infectious disease dynamics. A number of authors have studied clustered
networks, but the networks often contain preferential mixing between high
degree nodes. We introduce a class of random clustered networks and another
class of random unclustered networks with the same preferential mixing. We
analytically show that percolation in the clustered networks reduces the
component sizes and increases the epidemic threshold compared to the
unclustered networks.
