  The availability of data from many different sources and fields of science
has made it possible to map out an increasing number of networks of contacts
and interactions. However, quantifying how reliable these data are remains an
open problem. From Biology to Sociology and Economy, the identification of
false and missing positives has become a problem that calls for a solution. In
this work we extend one of newest, best performing models -due to Guimera and
Sales-Pardo in 2009- to directed networks. The new methodology is able to
identify missing and spurious directed interactions, which renders it
particularly useful to analyze data reliability in systems like trophic webs,
gene regulatory networks, communication patterns and social systems. We also
show, using real-world networks, how the method can be employed to help
searching for new interactions in an efficient way.
