Turing patterns in Matrix-Weighted Networks

TL;DR

This paper extends Turing pattern analysis to Matrix Weighted Networks, revealing how matrix weights and network topology influence pattern formation, and providing a framework for designing such patterns in complex systems.

Contribution

It introduces a novel characterization of coherence in MWNs, reduces matrix-weighted diffusion to scalar-weighted diffusion, and extends classical Turing instability analysis to these networks.

Findings

Derived conditions for Turing instability in MWNs.

Showed how network topology and matrix weights jointly affect patterns.

Provided a framework for analyzing and designing Turing patterns in complex networks.

Abstract

Diffusion-driven instability is a fundamental mechanism underlying pattern formation in spatially extended systems. In almost all existing works, diffusion across the links of the underlying network is modeled through scalar weights, possibly complemented by cross-diffusion terms that are homogeneous across links. In this work, we investigate the emergence of Turing patterns on Matrix Weighted Networks (MWNs), a recently introduced framework in which each edge is associated with a matrix weight. Focusing on the class of coherent MWNs, we provide a novel characterization of coherence in terms of node-dependent orthonormal matrices, showing that link transformations can be written as relative rotations between nodes. This representation allows us to deal with coherent MWNs of any size and to introduce an orthonormal change of variables capable to reduce diffusion on a coherent MWN to diffusion on a standard weighted network with scalar weights. Building on this, we extend the classical Turing instability analysis to MWNs and derive the conditions under which a homogeneous equilibrium of the local dynamics loses stability due to matrix-weighted diffusion. Our results show how network topology, scalar weights, and inter-node transformations jointly shape pattern formation, and provide a constructive framework to analyze and design Turing patterns on matrix-weighted and higher-order networked systems.