Analysis of community structure in networks of correlated data

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We present a reformulation of modularity that allows the analysis of the community structure in networks of correlated data. The new modularity preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops. This is the most general condition one can find in the study of any network, in particular those defined from correlated data. We apply our results to a real network of correlated data between stores in the city of Lyon (France).

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Gomez, S.; Jensen, P. & Arenas, A. December 25, 2008.

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We present a reformulation of modularity that allows the analysis of the community structure in networks of correlated data. The new modularity preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops. This is the most general condition one can find in the study of any network, in particular those defined from correlated data. We apply our results to a real network of correlated data between stores in the city of Lyon (France).

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  • Journal Name: Physical Review E

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  • Report No.: LBNL-3717E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 985902
  • Archival Resource Key: ark:/67531/metadc1014608

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  • December 25, 2008

Added to The UNT Digital Library

  • Oct. 14, 2017, 8:36 a.m.

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  • Oct. 17, 2017, 5:59 p.m.

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Gomez, S.; Jensen, P. & Arenas, A. Analysis of community structure in networks of correlated data, article, December 25, 2008; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc1014608/: accessed November 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.