22 citations · 32 across the 12 of their papers we have counts for
15 papers
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…
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 Barà, 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 Barà, 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…
Information-theoretic Quantification of High-order Feature Effects in Classification Problems
Ivan Lazic, Chiara Barà, Marta Iovino +3
Understanding the contribution of individual features in predictive models remains a central goal in interpretable machine learning, and while many model-agnostic methods exist to…
Localizing synergies of hidden factors across complex systems: resting brain networks and HeLa gene expression profile as case studies
Marlis Ontivero-Ortega, Gorana Mijatovic, Luca Faes +2
Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Eve…
A Method for the Time-Frequency Analysis of High-Order Interactions in Non-Stationary Physiological Networks
Yuri Antonacci, Chiara Bara', Laura Sparacino +3
Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions involving three or more components of a network system. Most of…
Predictive Information Decomposition as a Tool to Quantify Emergent Dynamical Behaviors In Physiological Networks
Luca Faes, Gorana Mijatovic, Laura Sparacino +1
Objective: This work introduces a framework for multivariate time series analysis aimed at detecting and quantifying collective emerging behaviors in the dynamics of physiological…
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…
Assessing high-order effects in feature importance via predictability decomposition
Marlis Ontivero-Ortega, Luca Faes, Jesus M Cortes +2
Leveraging the large body of work devoted in recent years to describe redundancy and synergy in multivariate interactions among random variables, we propose a novel approach to qua…
Measures and Models of Brain-Heart Interactions
Diego Candia-Rivera, Luca Faes, Fabrizio De Vico Fallani +1
The exploration of brain-heart interactions within various paradigms, including affective computing, human-computer interfaces, and sensorimotor evaluation, stands as a significant…
Partial information decomposition for mixed discrete and continuous random variables
Chiara Barà, Yuri Antonacci, Marta Iovino +2
The framework of Partial Information Decomposition (PID) unveils complex nonlinear interactions in network systems by dissecting the mutual information (MI) between a target variab…
A Model-Free Method to Quantify Memory Utilization in Neural Point Processes
Gorana Mijatovic, Sebastiano Stramaglia, Luca Faes
Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neur…
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…