Gradients of O-information: low-order descriptors of high-order dependencies

Autor:	Scagliarini, Tomas, Nuzzi, Davide, Antonacci, Yuri, Faes, Luca, Rosas, Fernando E., Marinazzo, Daniele, Stramaglia, Sebastiano
Rok vydání:	2022
Předmět:	Computer Science - Information Theory Physics - Data Analysis Statistics and Probability
Druh dokumentu:	Working Paper
Popis:	O-information is an information-theoretic metric that captures the overall balance between redundant and synergistic information shared by groups of three or more variables. To complement the global assessment provided by this metric, here we propose the gradients of the O-information as low-order descriptors that can characterise how high-order effects are localised across a system of interest. We illustrate the capabilities of the proposed framework by revealing the role of specific spins in Ising models with frustration, and on practical data analysis on US macroeconomic data. Our theoretical and empirical analyses demonstrate the potential of these gradients to highlight the contribution of variables in forming high-order informational circuits Comment: 4 pages + supplementary material, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.03581 Zobrazit plný text záznamu View this record from Arxiv