Disentangling categorical relationships through a graph of co-occurrences Articles uri icon

authors

  • MARTINEZ ROMO, JUAN
  • ARAUJO, LOURDES
  • BORGE-HOLTOEFER, JAVIER
  • ARENAS, ALEX
  • CAPITAN GOMEZ, JOSE ANGEL
  • CUESTA RUIZ, JOSE ANTONIO

publication date

  • October 2011

start page

  • 1

end page

  • 8

issue

  • 4 (046108)

volume

  • 84

International Standard Serial Number (ISSN)

  • 1539-3755

Electronic International Standard Serial Number (EISSN)

  • 1550-2376

abstract

  • The mesoscopic structure of complex networks has proven a powerful level of description to understand the linchpins of the system represented by the network. Nevertheless, themapping of a series of relationships between elements, in terms of a graph, is sometimes not straightforward. Given that all the information we would extract using complex network tools depend on this initial graph, it is mandatory to preprocess the data to build it on in the most accurate manner. Here we propose a procedure to build a network, attending only to statistically significant relations between constituents. We use a paradigmatic example of word associations to show the development of our approach. Analyzing the modular structure of the obtained network we are able to disentangle categorical relations, disambiguating words with success that is comparable to the best algorithms designed to the same end.

keywords

  • community structure; complex networks; random-wolks; algorithm