A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Community characterization of heterogeneous complex systems
Tekijät: Tumminello M, Micciche S, Lillo F, Varho J, Piilo J, Mantegna RN
Kustantaja: IOP PUBLISHING LTD
Julkaisuvuosi: 2011
Journal: Journal of Statistical Mechanics: Theory and Experiment
Tietokannassa oleva lehden nimi: JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Lehden akronyymi: J STAT MECH-THEORY E
Artikkelin numero: ARTN P01019
Sivujen määrä: 14
ISSN: 1742-5468
DOI: https://doi.org/10.1088/1742-5468/2011/01/P01019
Tiivistelmä
We introduce an analytical statistical method for characterizing the communities detected in heterogeneous complex systems. By proposing a suitable null hypothesis, our method makes use of the hypergeometric distribution to assess the probability that a given property is over-expressed in the elements of a community with respect to all the elements of the investigated set. We apply our method to two specific complex networks, namely a network of world movies and a network of physics preprints. The characterization of the elements and of the communities is done in terms of languages and countries for the movie network and of journals and subject categories for papers. We find that our method is able to characterize clearly the communities identified. Moreover our method works well both for large and for small communities.
We introduce an analytical statistical method for characterizing the communities detected in heterogeneous complex systems. By proposing a suitable null hypothesis, our method makes use of the hypergeometric distribution to assess the probability that a given property is over-expressed in the elements of a community with respect to all the elements of the investigated set. We apply our method to two specific complex networks, namely a network of world movies and a network of physics preprints. The characterization of the elements and of the communities is done in terms of languages and countries for the movie network and of journals and subject categories for papers. We find that our method is able to characterize clearly the communities identified. Moreover our method works well both for large and for small communities.
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