Missing and spurious interactions and the reconstruction of complex networks
Guimera R, Sales-Pardo MProc. Natl. Acad. Sci. U. S. A. 106, 22073 - 22078 (2009)
Times cited: 129
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 ?elds 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.