Interacting particle systems are known for their ability to generate large-scale self-organized
structures from simple local interaction rules between each agent and its neighbors. In …

This paper deals with the derivation of the mean-field limit for multi-agent systems on a large
class of sparse graphs. More specifically, the case of non-exchangeable multi-agent …

We propose an approach to modeling large-scale multi-agent dynamical systems allowing
interactions among more than just pairs of agents using the theory of mean field games and …

A particle system is said to be non-exchangeable if two particles cannot be exchanged
without modifying the overall dynamics. Because of this property, the classical mean-field …

A broad range of dynamical systems involve multibody interactions, or group interactions,
which may not be encoded in traditional graphical structures. In this work, we focus on a …

Hypergraphs have emerged as a powerful modeling framework to represent systems with
multiway interactions, that is systems where interactions may involve an arbitrary number of …

In the past 20 years network science has proven its strength in modeling many real-world
interacting systems as generic agents, the nodes, connected by pairwise edges …

Complex networked systems in fields such as physics, biology, and social sciences often
involve interactions that extend beyond simple pairwise ones. Hypergraphs serve as …

In the last twenty years, the modeling of complex systems has become a relevant domain of
study, not only in applied sciences, but also among mathematicians. The recent …

In this article, we study the large-population limit of interacting particle systems posed on
weighted random graphs. In that aim, we introduce a general framework for the construction …