Revealing Dynamics, Communities, and Criticality from Data

Author(s)

Eroglu, Deniz ^{[1, 2, 3]} ; Tanzi, Matteo ^{[1, 4]} ; van Strien, Sebastian ^[1] ; Pereira, Tiago ^{[1, 2]}

Affiliation

^[1] Imperial Coll London, Dept Math, London SW7 2AZ - England

^[2] Univ Sao Paulo, Inst Ciencias Matemat & Comp, BR-13566590 Sao Carlos - Brazil

^[3] Kadir Has Univ, Dept Bioinformat & Genet, TR-34083 Istanbul - Turkey

^[4] NYU, Courant Inst Math Sci, 251 Mercer St, New York, NY 10012 - USA

• Document type: Journal article
• Source: PHYSICAL REVIEW X; v. 10, n. 2 JUN 1 2020.
• Web of Science Citations: 0

Abstract

Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behavior for parameter ranges for which no data on the system are available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an effective network, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behavior of such networks can be decomposed in terms of an emergent deterministic component and a fluctuation term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. We illustrate this approach on synthetic time series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. We reconstruct the community structure by analyzing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC