  We demonstrate that a tree-based theory for various dynamical processes
yields extremely accurate results for several networks with high levels of
clustering. We find that such a theory works well as long as the mean
intervertex distance $\ell$ is sufficiently small - i.e., as long as it is
close to the value of $\ell$ in a random network with negligible clustering and
the same degree-degree correlations. We confirm this hypothesis numerically
using real-world networks from various domains and on several classes of
synthetic clustered networks. We present analytical calculations that further
support our claim that tree-based theories can be accurate for clustered
networks provided that the networks are "sufficiently small" worlds.
