Synchrony and Anti-synchrony in Weighted Networks

TL;DR

This paper characterizes the invariant synchrony and anti-synchrony subspaces in weighted networks, providing conditions based on the network's adjacency and Laplacian matrices, with implications for understanding network dynamics.

Contribution

It offers a novel characterization of synchrony and anti-synchrony subspaces in weighted networks, extending previous results to include these generalized polydiagonal conditions.

Findings

Identifies conditions for invariance of subspaces under adjacency and Laplacian matrices.

Provides a framework for analyzing flow-invariant subspaces in weighted networks.

Enhances understanding of how network structure influences dynamical behaviors.

Abstract

We consider weighted coupled cell networks, that is networks where the interactions between any two cells have an associated weight that is a real valued number. Weighted networks are ubiquitous in real-world applications. We consider a dynamical systems perspective by associating to each network a set of continuous dynamical systems, the ones that respect the graph structure of the network. For weighted networks it is natural for the admissible coupled cell systems to have an additive input structure. We present a characterization of the synchrony subspaces and the anti-synchrony subspaces for a weighted network depending on the restrictions that are imposed to their admissible input-additive coupled cell systems. These subspaces are flow-invariant by those systems and are generalized polydiagonal subspaces, that is, are characterized by conditions on the cell coordinates of the types $x_i = x_j$ and/or $x_k = -x_l$ and/or $x_m=0$. The existence and identification of the synchrony and anti-synchony subspaces for a weighted network are deeply relevant from the applications and dynamics point of view. Our characterization of the synchrony and anti-synchrony subspaces of a weighted network follows from our results where we give necessary and sufficient conditions for a generalized polydiagonal to be left invariant by the adjacency matrix and/or the Laplacian matrix of the network.