Replica-Mean-Field Limits of Fragmentation-Interaction-Aggregation Processes

TL;DR

This paper proves the Poisson Hypothesis for a broad class of complex network dynamics called fragmentation-interaction-aggregation processes, enabling analytical tractability through the replica-mean-field approach.

Contribution

The paper establishes the Poisson Hypothesis for a general class of discrete-time point-process networks, proving asymptotic independence in the replica-mean-field limit.

Findings

Poisson Hypothesis holds for the proposed network class.

Asymptotic independence propagates in the infinite replica limit.

Application to Galves-Löcherbach neural networks demonstrates the theory.

Abstract

Network dynamics with point-process-based interactions are of paramount modeling interest. Unfortunately, most relevant dynamics involve complex graphs of interactions for which an exact computational treatment is impossible. To circumvent this difficulty, the replica-mean-field approach focuses on randomly interacting replicas of the networks of interest. In the limit of an infinite number of replicas, these networks become analytically tractable under the so-called "Poisson Hypothesis". However, in most applications, this hypothesis is only conjectured. Here, we establish the Poisson Hypothesis for a general class of discrete-time, point-process-based dynamics, that we propose to call fragmentation-interaction-aggregation processes, and which are introduced in the present paper. These processes feature a network of nodes, each endowed with a state governing their random activation. Each activation triggers the fragmentation of the activated node state and the transmission of interaction signals to downstream nodes. In turn, the signals received by nodes are aggregated to their state. Our main contribution is a proof of the Poisson Hypothesis for the replica-mean-field version of any network in this class. The proof is obtained by establishing the propagation of asymptotic independence for state variables in the limit of an infinite number of replicas. Discrete time Galves-L\"{o}cherbach neural networks are used as a basic instance and illustration of our analysis.