Parameter estimation in interacting particle systems on dynamic random networks

TL;DR

This paper introduces a method to estimate the dynamics of interacting particle systems on evolving networks using limited snapshot data, demonstrating its effectiveness through numerical experiments.

Contribution

It presents a novel inference approach for dynamic random networks with one-way feedback, focusing on parameter estimation from partial observations.

Findings

Effective inference method demonstrated on simulated data

Accurate estimation of network and vertex dynamics achieved

Method applicable to systems with one-way feedback

Abstract

In this paper we consider a class of interacting particle systems on dynamic random networks, in which the joint dynamics of vertices and edges acts as one-way feedback, i.e., edges appear and disappear over time depending on the state of the two connected vertices, while the vertex dynamics does not depend on the edge process. Our goal is to estimate the underlying dynamics from partial information of the process, specifically from snapshots of the total number of edges present. We showcase the effectiveness of our inference method through various numerical results.