Data reliability in complex directed networks

TL;DR

This paper extends a leading model to directed networks, enabling the detection of false and missing interactions, thereby improving data reliability analysis in complex systems like biological, social, and economic networks.

Contribution

It introduces a novel extension of an existing model to directed networks, enhancing the ability to identify false positives and negatives in diverse data sources.

Findings

Effective identification of missing and spurious directed interactions

Application to real-world networks demonstrates practical utility

Facilitates efficient discovery of new interactions

Abstract

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.