Non-Negative Matrix Factorization for Link Prediction Preserving Row and Column Spaces

Non-negative Matrix Factorization (NMF) has been widely adopted for link prediction, aiming at finding multiple low-dimensional matrices whose product approximates the adjacency matrix of a network. Most existing NMF-based models incorporate auxiliary information with well-defined geometric meanings...

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Bibliographic Details
Published inProceedings (IEEE International Conference on Data Mining) pp. 1451 - 1456
Main Authors Yan, Liping, Yu, Weiren
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2023
Subjects
column space
Convergence
Data mining
link prediction
Mathematical models
non-negative matrix factorization
Predictive models
row space
Time complexity
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ISSN2374-8486
DOI10.1109/ICDM58522.2023.00190

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Summary:Non-negative Matrix Factorization (NMF) has been widely adopted for link prediction, aiming at finding multiple low-dimensional matrices whose product approximates the adjacency matrix of a network. Most existing NMF-based models incorporate auxiliary information with well-defined geometric meanings, but there is no evidence that they have reasonable mathematical interpretations. In this paper, we propose a model, NMF-CR, that incorporates both row-space and column-space information into the NMF framework. NMF-CR not only carries well-defined geometric meanings but also boasts a reasonable mathematical interpretation. Moreover, we provide efficient updating rules to infer the parameters of NMF-CR with guaranteed convergence. Extensive experiments demonstrate that our model achieves higher prediction accuracy than its competitors.
ISSN:2374-8486
DOI:10.1109/ICDM58522.2023.00190