Impact of lag information on network inference

Abstract

Extracting useful information from data is a fundamental challenge across disciplines as diverse as climate, neuroscience, genetics, and ecology. In the era of ``big data'', data is ubiquitous, but appropriated methods are needed for gaining reliable information from the data. In this work we consider a complex system, composed by interacting units, and aim at inferring which elements influence each other, directly from the observed data. The only assumption about the structure of the system is that it can be modeled by a network composed by a set of $N$ units connected with $L$ un-weighted and un-directed links, however, the structure of the connections is not known. In this situation the inference of the underlying network is usually done by using interdependency measures, computed from the output signals of the units. We show, using experimental data recorded from randomly coupled electronic R{ö}ssler chaotic oscillators, that the information of the lag times obtained from bivariate cross-correlation analysis can be useful to gain information about the real connectivity of the system.

References in corpus (14)

• ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
• Scale-free brain functional networks
• Complex networks in climate dynamics - Comparing linear and nonlinear network construction methods
• Revealing Network Connectivity From Dynamics
• Model-free inference of direct network interactions from nonlinear collective dynamics
• Evolving networks in the human epileptic brain
• Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
• Persistent homology of time-dependent functional networks constructed from coupled time series
• From brain to earth and climate systems: Small-world interaction networks or not?
• Reconstructing Links in Directed Networks from Noisy Dynamics
• Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes
• Reconstructing networks of pulse-coupled oscillators from spike trains
• Reconstruction of chaotic neural network from observed firing rates
• Successful network inference from time-series data using Mutual Information Rate

Cited by in corpus (1)

• Inferring the connectivity of coupled oscillators and anticipating their transition to synchrony through lag-time analysis