  Turing instability in complex networks have been shown in the literature to
be dominated by the distribution of the nodal degrees. The conditions for
Turing instability have been derived with an explicit dependence on the
eigenvalues of the Laplacian, which in turn depends on the network topology.
This study reveals that apart from average degree of the network, another
global network measure - the nodal clustering - also plays a crucial role.
Analytical and numerical results are presented to show the importance of
clustering for several network topologies ranging from the $\mathbb{S}^1$ /
$\mathbb{H}^2$ hyperbolic geometric networks that enable modelling the
naturally occurring clustering in real world networks, as well as the random
and scale free networks, which are obtained as limiting cases of the
$\mathbb{S}^1$ / $\mathbb{H}^2$ model. Analysis of eigenvector localization
properties in these networks are shown to reveal distinct signatures that
enable identifying the so called Turing patterns even in complex networks.
