Data reliability in complex directed networks

Joaqu\', ; {\i}n Sanz, ; Cozzo, Emanuele; Moreno, Yamir
The availability of data from many different sources and fields of$\backslash$nscience has made it possible to map out an increasing number of networks$\backslash$nof contacts and interactions. However, quantifying how reliable these$\backslash$ndata are remains an open problem. From Biology to Sociology and$\backslash$nEconomics, the identification of false and missing positives has become$\backslash$na problem that calls for a solution. In this work we extend one of the$\backslash$nnewest, best performing models-due to Guimera and Sales-Pardo in 2009-to$\backslash$ndirected networks. The new methodology is able to identify missing and$\backslash$nspurious directed interactions with more precision than previous$\backslash$napproaches, which renders it particularly useful for analyzing data$\backslash$nreliability in systems like trophic webs, gene regulatory networks,$\backslash$ncommunication patterns and several social systems. We also show, using$\backslash$nreal-world networks, how the method can be employed to help search for$\backslash$nnew interactions in an efficient way.
Research areas:
Year:
2014
Type of Publication:
Article
Keywords:
network reconstruction; networks; random graphs; regulatory networks (theory)
Journal:
Journal of Statistical Mechanics: Theory and Experiment
Volume:
2013
Pages:
P12008
ISSN:
1742-5468
DOI:
10.1088/1742-5468/2013/12/P12008
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