ArticleScientific reports2016
Weight prediction in complex networks based on neighbor set.
Article in Scientific reports, 2016. 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.
- Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata.Entropy (Basel, Switzerland) · 2022Article
- Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models.F1000Research · 2019Article
- Link Prediction based on Quantum-Inspired Ant Colony Optimization.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.
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Registered trials
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