7 papers
From Statistical to Structural Synergy: A Predictability Framework to Quantify the Effects due to High-Order Mechanisms
Yuri Antonacci, Chiara BarÃ, Laura Sparacino +3
High-order interactions are increasingly recognized as a hallmark of collective dynamics in complex systems. The relationship between high-order behaviours (HOBs), observed as syne…
Dissecting Spectral Granger Causality through Partial Information Decomposition
Luca Faes, Gorana Mijatovic, Riccardo Pernice +3
Granger causality (GC), a popular statistical method for the inference of directional influences between time series measured from a complex network, is sensitive to high-order (no…
Redundant and synergistic interactions in a complex network of single-transistor electronic chaotic oscillators and in neurophysiological recordings
Chiara BarÃ, Yuri Antonacci, Laura Sparacino +8
Complex networks often exhibit emergent behaviors, where simple dyadic interactions yield collective dynamics that cannot be explained by examining the system's units individually…
Investigating High-Order Behaviors in Multivariate Cardiovascular Interactions via Nonlinear Prediction and Information-Theoretic Tools
Chiara BarÃ, Yuri Antonacci, Laura Sparacino +4
Assessing the synergistic high-order behaviors (HOBs) that emerge from underlying structural mechanisms is crucial to characterize complex systems. This work leverages the combined…
Decomposing Multivariate Information Rates in Networks of Random Processes
Laura Sparacino, Gorana Mijatovic, Yuri Antonacci +4
The Partial Information Decomposition (PID) framework has emerged as a powerful tool for analyzing high-order interdependencies in complex network systems. However, its application…
Partial Information Rate Decomposition
Luca Faes, Laura Sparacino, Gorana Mijatovic +4
Partial Information Decomposition (PID) is a principled and flexible method to unveil complex high-order interactions in multi-unit network systems. Though being defined exclusivel…