ArticleProceedings of the National Academy of Sciences of the United States of America2009
Missing and spurious interactions and the reconstruction of complex networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
140 citing papers in PubMed.
- Minimum entropy collaborative groupings: A tool for an automatic heterogeneous learning group formation.PloS one · 2023Trial
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- Geometric deep learning enables high-fidelity network imputation for HIV transmission modeling.Communications medicine · 2026Article
- Predicting missing links in food webs using stacked models and species traits.Nature communications · 2026Article
- Drug-disease networks and drug repurposing.PLoS computational biology · 2025Article
- A causal framework for the drivers of animal social network structure.PLoS computational biology · 2025Article
- Deciphering Probabilistic Species Interaction Networks.Ecology letters · 2025Review
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm.PloS one · 2025Article
- Inconsistency among evaluation metrics in link prediction.PNAS nexus · 2024Article
- A One Health framework for exploring zoonotic interactions demonstrated through a case study.Nature communications · 2024Article
- Community detection in the human connectome: Method types, differences and their impact on inference.Human brain mapping · 2024Article
- The maximum capability of a topological feature in link prediction.PNAS nexus · 2024Article
- On the complexity of quantum link prediction in complex networks.Scientific reports · 2024Article
- Link prediction based on spectral analysis.PloS one · 2024Article
- The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data.Frontiers in public health · 2024Article
- Hyperedge prediction and the statistical mechanisms of higher-order and lower-order interactions in complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Reconstructing signed relations from interaction data.Scientific reports · 2023Article
- Triadic influence as a proxy for compatibility in social relationships.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.