  Networks provide useful tools for analyzing diverse complex systems from
natural, social, and technological domains. Growing size and variety of data
such as more nodes and links and associated weights, directions, and signs can
provide accessory information. Link and weight abundance, on the other hand,
results in denser networks with noisy, insignificant, or otherwise redundant
data. Moreover, typical network analysis and visualization techniques
presuppose sparsity and are not appropriate or scalable for dense and weighted
networks. As a remedy, network backbone extraction methods aim to retain only
the important links while preserving the useful and elucidative structure of
the original networks for further analyses. Here, we provide the first methods
for extracting signed network backbones from intrinsically dense unsigned
unipartite weighted networks. Utilizing a null model based on statistical
techniques, the proposed significance filter and vigor filter allow inferring
edge signs. Empirical analysis on migration, voting, temporal interaction, and
species similarity networks reveals that the proposed filters extract
meaningful and sparse signed backbones while preserving the multiscale nature
of the network. The resulting backbones exhibit characteristics typically
associated with signed networks such as reciprocity, structural balance, and
community structure. The developed tool is provided as a free, open-source
software package.
