SCIENCE@home

Artificial Life and Evolutionary Computation

S. Cagnoni,M. Mordonini,Riccardo Pecori,A. Roli,M. Villani

Published 2018 in Communications in Computer and Information Science

ABSTRACT

We present an improvement of a method that aims at detecting important dynamical structures in complex systems, by identifying subsets of elements that show tight and coordinated interactions among themselves, while interplaying much more loosely with the rest of the system. Such subsets are estimated by means of a Relevance Index (RI), which is normalized with respect to a homogeneous system, usually described by independent Gaussian variables, as a reference. The strategy presented herein improves the way the homogeneous system is conceived from a theoretical viewpoint. Firstly, we consider the system components as dependent and with equal pairwise correlations, which implies a non-diagonal correlation matrix of the homogeneous system. Then, we generate the components of the homogeneous system according to a multivariate Bernoulli distribution, by exploiting the NORTA method, which is able to create samples of a desired random vector, given its marginal distributions and its correlation matrix. The proposed improvement on the RI method has been applied to three different case studies, obtaining better results compared with the traditional method based on the homogeneous system with independent Gaussian variables.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    Communications in Computer and Information Science

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.1007/978-3-030-21733-4
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-86 of 86 references · Page 1 of 1

CITED BY