Parameter estimation in a dynamic Chung-Lu random graph

TL;DR

This paper introduces a method to estimate the underlying dynamics of a dynamic Chung-Lu random graph using partial information, demonstrated through numerical experiments.

Contribution

It presents a novel technique for inferring the evolution of a dynamic Chung-Lu graph from limited data, specifically snapshots of total edges.

Findings

Effective estimation of graph dynamics from partial data

Method performs well in numerical experiments

Provides a new tool for analyzing dynamic networks

Abstract

In this paper we consider a dynamic version of the Chung-Lu random graph in which the edges alternate between being present and absent. The main contribution concerns a technique by which one can estimate the underlying dynamics from partial information, in particular from snapshots of the total number of edges present. The efficacy of our inference method is demonstrated through a series of numerical experiments.