Analytic relationship of relative synchronizability to network structure and motifs

• Published in: Proceedings of the National Academy of Sciences - PNAS 2023-09, Vol.120 (37), p.e2303332120
• Main Authors: Lizier, Joseph T, Bauer, Frank, Atay, Fatihcan M, Jost, Jürgen
• Format: Article
• Language: English
• Subjects: Autoregressive processes; Covariance matrix; Dynamic structural analysis; Mathematical analysis; Networks; Nonlinear systems; Physical Sciences; Relative abundance; Synchronism; Synchronization; Time synchronization
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2303332120

Synchronization phenomena on networks have attracted much attention in studies of neural, social, economic, and biological systems, yet we still lack a systematic understanding of how relative synchronizability relates to underlying network structure. Indeed, this question is of central importance to the key theme of how dynamics on networks relate to their structure more generally. We present an analytic technique to directly measure the relative synchronizability of noise-driven time-series processes on networks, in terms of the directed network structure. We consider both discrete-time autoregressive processes and continuous-time Ornstein-Uhlenbeck dynamics on networks, which can represent linearizations of nonlinear systems. Our technique builds on computation of the network covariance matrix in the space orthogonal to the synchronized state, enabling it to be more general than previous work in not requiring either symmetric (undirected) or diagonalizable connectivity matrices and allowing arbitrary self-link weights. More importantly, our approach quantifies the relative synchronization specifically in terms of the contribution of process motif (walk) structures. We demonstrate that in general the relative abundance of process motifs with convergent directed walks (including feedback and feedforward loops) hinders synchronizability. We also reveal subtle differences between the motifs involved for discrete or continuous-time dynamics. Our insights analytically explain several known general results regarding synchronizability of networks, including that small-world and regular networks are less synchronizable than random networks.