A1 Refereed original research article in a scientific journal
Community characterization of heterogeneous complex systems
Authors: Tumminello M, Micciche S, Lillo F, Varho J, Piilo J, Mantegna RN
Publisher: IOP PUBLISHING LTD
Publication year: 2011
Journal: Journal of Statistical Mechanics: Theory and Experiment
Journal name in source: JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
Journal acronym: J STAT MECH-THEORY E
Article number: ARTN P01019
Number of pages: 14
ISSN: 1742-5468
DOI: https://doi.org/10.1088/1742-5468/2011/01/P01019
Abstract
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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