Scaling of the Clustering Function in Spatial Inhomogeneous Random Graphs

We consider an infinite spatial inhomogeneous random graph model with integrable connection kernel that interpolates nicely between existing models. Key examples are versions of the weight-dependent model, geometric graph, and age-based model. These models arise as local limit corresponding finite models, see \cite{LWC_SIRGs_2020}. For these we identify scaling \emph{local clustering} a function degree root in different regimes unified way. show exhibits phase transitions interpolation parameter moves across regimes. In addition to also leading constants clustering function. This allows us draw conclusions on geometry \emph{typical} triangle contributing