ArticleProceedings of the National Academy of Sciences of the United States of America2020
Stacking models for nearly optimal link prediction in complex networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 148 citations in OpenAlex.
- Predictability of complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (NProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Predicting missing links in food webs using stacked models and species traits.Nature communications · 2026Article
- A multi-model fusion approach incorporating conventional radiological and machine learning features across age spectrum for periorbital fat status prediction.Frontiers in medicine · 2026Article
- scGALA advances graph link prediction-based cell alignment for comprehensive data integration and harmonization.Nature communications · 2025Article
- Acoustic impedance inversion via voting stacked regression (VStaR) algorithms.Scientific reports · 2025Article
- Determining interaction directionality in complex biochemical networks from stationary measurements.Scientific reports · 2025Article
- Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm.PloS one · 2025Article
- Network community detection via neural embeddings.Nature communications · 2024Article
- Inconsistency among evaluation metrics in link prediction.PNAS nexus · 2024Article
- The maximum capability of a topological feature in link prediction.PNAS nexus · 2024Article
- Link prediction using low-dimensional node embeddings: The measurement problem.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Fusing graph transformer with multi-aggregate GCN for enhanced drug-disease associations prediction.BMC bioinformatics · 2024Article
- Sequential stacking link prediction algorithms for temporal networks.Nature communications · 2024Article
- On the complexity of quantum link prediction in complex networks.Scientific reports · 2024Article
- Link prediction accuracy on real-world networks under non-uniform missing-edge patterns.PloS one · 2024Article
- Abstract cognitive maps of social network structure aid adaptive inference.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs.Scientific reports · 2023Article
- Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations.Biomedical engineering online · 2023Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
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5 authors at 3 institutions in 1 country.
Funding
Abstract
Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of "stacked" models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.
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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.