ArticleCommunications physics2022
Discrimination reveals reconstructability of multiplex networks from partial observations.
Article in Communications physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 14 citations in OpenAlex.
- Geometric deep learning enables high-fidelity network imputation for HIV transmission modeling.Communications medicine · 2026Article
- On the min-entropy of equilibrium graphs in network creation games.Scientific reports · 2026Article
- Reconstructing Sparse Multiplex Networks with Application to Covert Networks.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
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Abstract
An excellent method for predicting links in multiplex networks is reflected in its ability to reconstruct them accurately. Although link prediction methods perform well on estimating the existence probability of each potential link in monoplex networks by the set of partially observed links, we lack a mathematical tool to reconstruct the multiplex network from the observed aggregate topology and partially observed links in multiplex networks. Here, we fill this gap by developing a theoretical and computational framework that builds a probability space containing possible structures with a maximum likelihood estimation. Then, we discovered that the discrimination, an indicator quantifying differences between layers from an entropy perspective, determines the reconstructability, i.e., the accuracy of such reconstruction. This finding enables us to design the optimal strategy to allocate the set of observed links in different layers for promoting the optimal reconstruction of multiplex networks. Finally, the theoretical analyses are corroborated by empirical results from biological, social, engineered systems, and a large volume of synthetic networks.
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Registered trials
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