Missing and spurious interactions and the reconstruction of complex networks

TL;DR

This paper introduces a mathematical and computational framework for identifying missing and spurious interactions in noisy complex network data, improving the accuracy of network reconstructions and true network property estimates.

Contribution

The authors develop a novel method to reliably detect errors and reconstruct true networks from noisy observations, enhancing data reliability in complex network analysis.

Findings

Effective identification of missing interactions

Detection of spurious links in noisy data

More accurate network property estimation

Abstract

Network analysis is currently used in a myriad of contexts: from identifying potential drug targets to predicting the spread of epidemics and designing vaccination strategies, and from finding friends to uncovering criminal activity. Despite the promise of the network approach, the reliability of network data is a source of great concern in all fields where complex networks are studied. Here, we present a general mathematical and computational framework to deal with the problem of data reliability in complex networks. In particular, we are able to reliably identify both missing and spurious interactions in noisy network observations. Remarkably, our approach also enables us to obtain, from those noisy observations, network reconstructions that yield estimates of the true network properties that are more accurate than those provided by the observations themselves. Our approach has the potential to guide experiments, to better characterize network data sets, and to drive new discoveries.