Observability transitions in clustered networks

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