  We investigate the effect of clustering on network observability transitions.
In the observability model introduced by Yang, Wang, and Motter [Phys. Rev.
Lett. 109, 258701 (2012)], a given fraction of nodes are chosen randomly, and
they and those neighbors are considered to be observable, while the other nodes
are unobservable. For the observability model on random clustered networks, we
derive the normalized sizes of the largest observable component (LOC) and
largest unobservable component (LUC). Considering the case where the numbers of
edges and triangles of each node are given by the Poisson distribution, we find
that both LOC and LUC are affected by the network's clustering: more
highly-clustered networks have lower critical node fractions for forming
macroscopic LOC and LUC, but this effect is small, becoming almost negligible
unless the average degree is small. We also evaluate bounds for these critical
points to confirm clustering's weak or negligible effect on the network
observability transition. The accuracy of our analytical treatment is confirmed
by Monte Carlo simulations.
