# Functional Dynamical Structures in Complex Systems: an Information-Theoretic Approach

## TL;DR

This paper introduces an information-theoretic method to analyze complex systems' dynamics without prior knowledge of their network topology, identifying influential groups and functional structures.

## Contribution

It develops the Dynamical Cluster Index and D-index, novel measures for detecting relevant and influential subsets in complex systems using only dynamical data.

## Key findings

- Effective detection of relevant subsets in artificial systems
- Identification of influential groups in natural systems
- Method does not require network topology knowledge

## Abstract

Understanding the dynamical behavior of complex systems is of exceptional relevance in everyday life, from biology to economy. In order to describe the dynamical organization of complex systems, existing methods require the knowledge of the network topology. By contrast, in this thesis we develop a new method based on Information Theory which does not require any topological knowledge. We introduce the Dynamical Cluster Index to detect those groups of system elements which have strong mutual interactions, named as Relevant Subsets. Among them, we identify those which exchange most information with the rest of the system, thus being the most influential for its dynamics. In order to detect such Functional Dynamical Structures, we introduce another information theoretic measure, called D-index. The experimental results make us confident that our method can be effectively used to study both artificial and natural complex systems.