We investigate the effect of clustering on network observability transitions. In the observability model introduced by Yang et al. (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.

我们调查了群集对网络可观察性过渡的影响。在Yang等人介绍的可观察性模型中。（2012年），节点的给定部分是随机选择的，并且它们和那些邻居被认为是可观察的，而其他节点是不可观察的。对于随机聚类网络上的可观察性模型，我们导出最大可观察分量（LOC）和最大不可观察分量（LUC）的规范化大小。考虑到每个节点的边和三角形的数量由泊松分布给出的情况，我们发现LOC和LUC都受网络群集的影响：高度聚集的网络对于形成宏观LOC和LUC具有较低的关键节点分数，但是这种影响很小，除非平均程度很小，否则几乎可以忽略不计。我们还评估了这些临界点的边界，以确认聚类对网络可观察性过渡的弱或可忽略的影响。蒙特卡洛模拟证实了我们分析处理的准确性。