ArticleScientific reports2016
Predicting missing links in complex networks based on common neighbors and distance.
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 10 papers.
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Who cites it
10 citing papers in PubMed.
- Graph-based machine learning model for weight prediction in protein-protein networks.BMC bioinformatics · 2024Article
- A Survey on Hyperlink Prediction.IEEE transactions on neural networks and learning systems · 2024Article
- Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score.Entropy (Basel, Switzerland) · 2024Article
- The maximum capability of a topological feature in link prediction.PNAS nexus · 2024Article
- Link prediction in complex network using information flow.Scientific reports · 2023Article
- Similarity-based link prediction in social networks using latent relationships between the users.Scientific reports · 2020Article
- Path-based extensions of local link prediction methods for complex networks.Scientific reports · 2020Article
- Network effects govern the evolution of maritime trade.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Label propagation method based on bi-objective optimization for ambiguous community detection in large networks.Scientific reports · 2019Article
- Predicting biomedical relationships using the knowledge and graph embedding cascade model.PloS one · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The algorithms based on common neighbors metric to predict missing links in complex networks are very popular, but most of these algorithms do not account for missing links between nodes with no common neighbors. It is not accurate enough to reconstruct networks by using these methods in some cases especially when between nodes have less common neighbors. We proposed in this paper a new algorithm based on common neighbors and distance to improve accuracy of link prediction. Our proposed algorithm makes remarkable effect in predicting the missing links between nodes with no common neighbors and performs better than most existing currently used methods for a variety of real-world networks without increasing complexity.
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Registered trials
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