activity
20202026
most citedLocal Granger Causality

16 citations · 27 across the 14 of their papers we have counts for

collaborators
Showing stat.MEShow all

8 papers · 1 filter

stat.ME2026

From Statistical to Structural Synergy: A Predictability Framework to Quantify the Effects due to High-Order Mechanisms

Yuri Antonacci, Chiara Barà, 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…

stat.ME2026

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…

stat.ME2025

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…

stat.ME2025★ 1 cited

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…

stat.ME2024

Network Representation of Higher-Order Interactions Based on Information Dynamics

Gorana Mijatovic, Yuri Antonacci, Michal Javorka +3

Many complex systems in science and engineering are modeled as networks whose nodes and links depict the temporal evolution of each system unit and the dynamic interaction between…

stat.ME2024★ 3 cited

Measuring hierarchically-organized interactions in dynamic networks through spectral entropy rates: theory, estimation, and illustrative application to physiological networks

Laura Sparacino, Yuri Antonacci, Gorana Mijatovic +1

Recent advances in signal processing and information theory are boosting the development of new approaches for the data-driven modelling of complex network systems. In the fields o…

stat.ME2024★ 1 cited

Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures

Gorana Mijatovic, Laura Sparacino, Yuri Antonacci +4

The network representation is becoming increasingly popular for the description of cardiovascular interactions based on the analysis of multiple simultaneously collected variables.…

stat.ME2022★ 5 cited

A Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Brain and Physiological Networks

Luca Faes, Gorana Mijatovic, Yuri Antonacci +7

While the standard network description of complex systems is based on quantifying links between pairs of system units, higher-order interactions (HOIs) involving three or more unit…