Missing and spurious interactions and the reconstruction of complex networks

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.

References in corpus (5)

• Synchronization in complex networks
• Hierarchical structure and the prediction of missing links in networks
• Extracting the hierarchical organization of complex systems
• Classes of complex networks defined by role-to-role connectivity profiles
• Detection of node group membership in networks with group overlap

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• Parsimonious module inference in large networks
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• Network-based confidence scoring system for genome-scale metabolic reconstructions
• Robustness of Link-prediction Algorithm Based on Similarity and Application to Biological Networks