Networks are often used to represent the relations among the variables of a dynamical system. The properties of network topology are usually exploited to understand the organization of the system. Nevertheless, the dynamical organization of a system might considerably differ from its topological one. In this paper, we describe a method to identify ``relevant subsets'' of variables. The variables belonging to a relevant subset should be strongly integrated and should have a much weaker interaction with the other system variables. Extending previous works on neural networks, an information-theoretic measure is introduced, i.e., the Dynamical Cluster Index, in order to identify candidate relevant subsets. The method solely relies on observations of the variables' values in time.
CITATION STYLE
Roli, A., Villani, M., Filisetti, A., & Serra, R. (2016). Beyond Networks: Search for Relevant Subsets in Complex Systems (pp. 127–134). https://doi.org/10.1007/978-3-319-24391-7_12
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