A positive statistical benchmark to assess network agreement

Bingjie Hao and István A. Kovács ()
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Bingjie Hao: Northwestern University
István A. Kovács: Northwestern University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Current computational methods for validating experimental network datasets compare overlap, i.e., shared links, with a reference network using a negative benchmark. However, this fails to quantify the level of agreement between the two networks. To address this, we propose a positive statistical benchmark to determine the maximum possible overlap between networks. Our approach can efficiently generate this benchmark in a maximum entropy framework and provides a way to assess whether the observed overlap is significantly different from the best-case scenario. We introduce a normalized overlap score, Normlap, to enhance comparisons between experimental networks. As an application, we compare molecular and functional networks, resulting in an agreement network of human as well as yeast network datasets. The Normlap score can improve the comparison between experimental networks by providing a computational alternative to network thresholding and validation.

Date: 2023
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DOI: 10.1038/s41467-023-38625-z

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