Central Limit Theorems for Linear Eigenvalue Statistics of Random Geometric Graphs
ABSTRACT
Random geometric graphs are fundamental models of spatial networks, obtained by connecting nearby points of a Poisson point process. In contrast to Erdős–Rényi graphs, the underlying geometry induces strong local dependencies between edges, making spectral analysis substantially more difficult. In this talk, I will present central limit theorems for linear eigenvalue statistics of random geometric graphs. The proof combines techniques from stochastic geometry and Malliavin–Stein normal approximation.
