ArticlePloS one2017
Link prediction based on non-negative matrix factorization.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm.PloS one · 2025Article
- Link Prediction in Dynamic Social Networks Combining Entropy, Causality, and a Graph Convolutional Network Model.Entropy (Basel, Switzerland) · 2024Article
- BERT4FCA: A method for bipartite link prediction using formal concept analysis and BERT.PloS one · 2024Article
- Link prediction on bipartite networks using matrix factorization with negative sample selection.PloS one · 2023Article
- Link Prediction through Deep Generative Model.iScience · 2020Article
- Discovering Links Between Side Effects and Drugs Using a Diffusion Based Method.Scientific reports · 2019Article
- A unified framework for link prediction based on non-negative matrix factorization with coupling multivariate information.PloS one · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid expansion of internet, the complex networks has become high-dimensional, sparse and redundant. Besides, the problem of link prediction in such networks has also obatined increasingly attention from different types of domains like information science, anthropology, sociology and computer sciences. It makes requirements for effective link prediction techniques to extract the most essential and relevant information for online users in internet. Therefore, this paper attempts to put forward a link prediction algorithm based on non-negative matrix factorization. In the algorithm, we reconstruct the correlation between different types of matrix through the projection of high-dimensional vector space to a low-dimensional one, and then use the similarity between the column vectors of the weight matrix as the scoring matrix. The experiment results demonstrate that the algorithm not only reduces data storage space but also effectively makes the improvements of the prediction performance during the process of sustaining a low time complexity.
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Registered trials
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