Mean field limit for interacting systems on co-evolving networks

TL;DR

This paper develops a mean-field framework for large systems of interacting particles on co-evolving networks, capturing complex dynamics including memory effects and non-linear weight interactions.

Contribution

It introduces a general mean-field description for particle systems on adaptive networks with non-local in time interactions and non-linear weight dynamics.

Findings

Provides a rigorous mean-field limit for co-evolving network systems

Applies to systems with memory effects and non-linear weights

Enhances understanding of collective behavior in adaptive networks

Abstract

Interacting particle systems are in frequent use to model collective behaviour in various situations and applications. For many systems, the interaction between the agents is restricted to an underlying network structure and often, the latter also evolves in time with its dynamics coupled to the evolution of the particles. Due to their relevance for applications such systems on adaptive or co-evolutionary networks have received increasing interest in recent years. In particular, a fundamental question concerns the behaviour of the system in the infinite particle limit. In this work we provide a mean-field description for a general particle system which exhibits non-locality in time (memory). The result applies particularly to a large class of systems on co-evolving networks including non-linear weight dynamics.