Singular value decomposition-based graph densification for link prediction in sparse graphs.
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| Title: | Singular value decomposition-based graph densification for link prediction in sparse graphs. |
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| Authors: | Pouria, Amir Hossein1 (AUTHOR), Chehreghani, Mostafa Haghir1 (AUTHOR), Bagheri, Alireza1 (AUTHOR) |
| Source: | Computer Journal. May2026, Vol. 69 Issue 5, p773-785. 13p. |
| Subjects: | Singular value decomposition, Sparse graphs, Graph connectivity, Boosting algorithms, Graph neural networks |
| Abstract: | Link prediction is a crucial task in complex network analysis, aiming to predict future connections between nodes in a graph. This problem is particularly challenging in sparse networks, where the low number of edges complicates traditional prediction methods. To address this, we propose singular value decomposition-graph attention network-gradient boosting (SVD-GAT-GB), a hybrid approach that densifies the sparse graph using truncated SVD, improves node representations through a GAT, and applies GB for accurate link prediction. Our method effectively handles the sparsity issue, significantly enhancing the prediction accuracy by leveraging structural information extracted from the densified graph. We validate our approach through extensive experiments on multiple datasets, demonstrating improvements in both F1-score and AUC by approximately 32% and 11%, respectively, over existing methods. The proposed model proves to be accurate and robust across diverse types of networks, making it an effective solution for link prediction in sparse graphs. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194431627 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Singular value decomposition-based graph densification for link prediction in sparse graphs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pouria%2C+Amir+Hossein%22">Pouria, Amir Hossein</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chehreghani%2C+Mostafa+Haghir%22">Chehreghani, Mostafa Haghir</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bagheri%2C+Alireza%22">Bagheri, Alireza</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Journal%22">Computer Journal</searchLink>. May2026, Vol. 69 Issue 5, p773-785. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Singular+value+decomposition%22">Singular value decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+graphs%22">Sparse graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+connectivity%22">Graph connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Link prediction is a crucial task in complex network analysis, aiming to predict future connections between nodes in a graph. This problem is particularly challenging in sparse networks, where the low number of edges complicates traditional prediction methods. To address this, we propose singular value decomposition-graph attention network-gradient boosting (SVD-GAT-GB), a hybrid approach that densifies the sparse graph using truncated SVD, improves node representations through a GAT, and applies GB for accurate link prediction. Our method effectively handles the sparsity issue, significantly enhancing the prediction accuracy by leveraging structural information extracted from the densified graph. We validate our approach through extensive experiments on multiple datasets, demonstrating improvements in both F1-score and AUC by approximately 32% and 11%, respectively, over existing methods. The proposed model proves to be accurate and robust across diverse types of networks, making it an effective solution for link prediction in sparse graphs. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1093/comjnl/bxaf144 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 773 Subjects: – SubjectFull: Singular value decomposition Type: general – SubjectFull: Sparse graphs Type: general – SubjectFull: Graph connectivity Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Graph neural networks Type: general Titles: – TitleFull: Singular value decomposition-based graph densification for link prediction in sparse graphs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pouria, Amir Hossein – PersonEntity: Name: NameFull: Chehreghani, Mostafa Haghir – PersonEntity: Name: NameFull: Bagheri, Alireza IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00104620 Numbering: – Type: volume Value: 69 – Type: issue Value: 5 Titles: – TitleFull: Computer Journal Type: main |
| ResultId | 1 |
