Unraveling the mesoscale organization induced by network-driven processes

• 成果类型: Article
• 署名作者: Barzon, Giacomo; Artime, Oriol; Suweis, Samir; De Domenico, Manlio
• 署名单位: University of Padua; Fondazione Bruno Kessler; University of Barcelona; University of Barcelona; Universitat de les Illes Balears; University of Padua; Istituto Nazionale di Fisica Nucleare (INFN); University of Padua
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10908
• DOI: 10.1073/pnas.2317608121
• 发表日期: 2024-07-09
• 关键词: functional connectivity; complex; brain; resilience

摘要

Complex systems are characterized by emergent patterns created by the nontrivial interplay between dynamical processes and the networks of interactions on which these processes unfold. Topological or dynamical descriptors alone are not enough to fully embrace this interplay in all its complexity, and many times one has to resort to dynamics-specific approaches that limit a comprehension of general principles. To address this challenge, we employ a metric-that we name Jacobian distance- which captures the spatiotemporal spreading of perturbations, enabling us to uncover the latent geometry inherent in network-driven processes. We compute the Jacobian distance for a broad set of nonlinear dynamical models on synthetic and real-world networks of high interest for applications from biological to ecological and social contexts. We show, analytically and computationally, that the process-driven latent geometry of a complex network is sensitive to both the specific features of the dynamics and the topological properties of the network. This translates into potential mismatches between the functional and the topological mesoscale organization, which we explain by means of the spectrum of the Jacobian matrix. Finally, we demonstrate that the Jacobian distance offers a clear advantage with respect to traditional methods when studying human brain networks. In particular, we show that it outperforms classical network communication models in explaining functional communities from structural data, therefore highlighting its potential in linking structure and function in the brain.