Source-linked AI summary
Collective phenomena emerging from the interactions between dynamical processes in multiplex networks
Vincenzo Nicosia, Per Sebastian Skardal, Alex Arenas, Vito Latora
TL;DR
The paper addresses the limited study of interacting dynamical processes by modeling them as coupled layers in a multiplex network. In a neural-synchronization and transport example, this coupling produces explosive synchronization and heterogeneous walker distributions, with analytical results reproducing simulations.
Problem
Most studies consider a single dynamical process on a single network, leaving interactions between simultaneous processes insufficiently addressed.
Source: p. 1Method
A multiplex framework couples distinct processes through cross-layer state-dependent parameters, demonstrated with Kuramoto synchronization and biased random-walk transport.
Source: pp. 1–2Results
The coupling produces spontaneous explosive synchronization, bistability, and a transition from homogeneous to heterogeneous random-walker distributions.
Source: pp. 3–4Takeaways & Limitations
Intertwining dynamical processes can generate collective phenomena absent from the uncoupled systems, and the analytical theory accurately reproduces the observed behavior.
Source: p. 4The simulations use instantaneous relaxation for simplicity, although the reported phenomena persist for finite relaxation times.
Source: p. 3
Abstract
from arXiv · showhide
We introduce a framework to intertwine dynamical processes of different nature, each with its own distinct network topology, using a multilayer network approach. As an example of collective phenomena emerging from the interactions of multiple dynamical processes, we study a model where neural dynamics and nutrient transport are bidirectionally coupled in such a way that the allocation of the transport process at one layer depends on the degree of synchronization at the other layer, and vice versa. We show numerically, and we prove analytically, that the multilayer coupling induces a spontaneous explosive synchronization and a heterogeneous distribution of allocations, otherwise not present in the two systems considered separately. Our framework can find application to other cases where two or more dynamical processes such as synchronization, opinion formation, information diffusion, or disease spreading, are interacting with each other.