ArticleScientific reports2017
Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Neighbor-Enhanced Link Prediction in Bipartite Networks.Entropy (Basel, Switzerland) · 2025Article
- Link prediction on bipartite networks using matrix factorization with negative sample selection.PloS one · 2023Article
- Research on cloud manufacturing service recommendation based on graph neural network.PloS one · 2023Article
- Identifying potential association on gene-disease network via dual hypergraph regularized least squares.BMC genomics · 2021Article
- QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion.Frontiers in genetics · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.
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Registered trials
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