Functional central limit theorem for subgraph counts in a dynamic random connection model

TL;DR

This paper proves a functional central limit theorem for subgraph counts in a dynamic random connection model, extending the cumulant method to handle the model's temporal dynamics.

Contribution

It introduces a dynamic extension of the cumulant method to establish tightness in the functional central limit theorem for the model.

Findings

Established a functional CLT for subgraph counts in the dynamic model

Developed a dynamic cumulant method for tightness

Provides theoretical foundation for analyzing temporal random graphs

Abstract

We prove a functional central limit theorem for subgraph counts in a dynamic version of the random connection model. To establish tightness, we develop a dynamic extension of the cumulant method.