ArticleScientific reports2018
Similarity-based future common neighbors model for link prediction in complex networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Interactomics: Dissecting Protein Networks in Living Systems.Advances in experimental medicine and biology · 2026Review
- Graph-based machine learning model for weight prediction in protein-protein networks.BMC bioinformatics · 2024Article
- A Link Prediction Algorithm Based on Weighted Local and Global Closeness.Entropy (Basel, Switzerland) · 2023Article
- Mapping the protein-protein interactome in the tumor immune microenvironment.Antibody therapeutics · 2023Review
- Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations.Entropy (Basel, Switzerland) · 2021Article
- Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding.Frontiers in genetics · 2021Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Link prediction aims to predict the existence of unknown links via the network information. However, most similarity-based algorithms only utilize the current common neighbor information and cannot get high enough prediction accuracy in evolving networks. So this paper firstly defines the future common neighbors that can turn into the common neighbors in the future. To analyse whether the future common neighbors contribute to the current link prediction, we propose the similarity-based future common neighbors (SFCN) model for link prediction, which accurately locate all the future common neighbors besides the current common neighbors in networks and effectively measure their contributions. We also design and observe three MATLAB simulation experiments. The first experiment, which adjusts two parameter weights in the SFCN model, reveals that the future common neighbors make more contributions than the current common neighbors in complex networks. And two more experiments, which compares the SFCN model with eight algorithms in five networks, demonstrate that the SFCN model has higher accuracy and better performance robustness.
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Registered trials
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