Abstract
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.
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- 1.
Results presented in a contribution currently under review.
- 2.
As an ongoing work, we are experimenting with a genetic algorithm for searching the CRS with highest DCI value for each size.
- 3.
The following three examples has already been described in [13], however in this contribution we emphasize different aspects w.r.t. previous work.
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Acknowledgements
The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no 284625.
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Roli, A., Villani, M., Filisetti, A., Serra, R. (2016). Beyond Networks: Search for Relevant Subsets in Complex Systems. In: Minati, G., Abram, M., Pessa, E. (eds) Towards a Post-Bertalanffy Systemics. Contemporary Systems Thinking. Springer, Cham. https://doi.org/10.1007/978-3-319-24391-7_12
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DOI: https://doi.org/10.1007/978-3-319-24391-7_12
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