Reconstructing networks

TL;DR

This paper reviews methods for reconstructing incomplete or hidden complex networks, focusing on statistical physics and information theory approaches across different scales of network analysis.

Contribution

It provides a comprehensive overview of network reconstruction techniques, emphasizing inference methods based on statistical physics and information theory.

Findings

Overview of reconstruction methods for different network scales

Focus on statistical physics and information theory approaches

Guidance on reconstructing macroscopic, mesoscale, and microscopic network features

Abstract

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory. The discussion will be organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.