DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization
A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-03-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2017.9020002 |
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| _version_ | 1832572977853497344 |
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| author | Nahla Mohamed Ahmed Ling Chen Yulong Wang Bin Li Yun Li Wei Liu |
| author_facet | Nahla Mohamed Ahmed Ling Chen Yulong Wang Bin Li Yun Li Wei Liu |
| author_sort | Nahla Mohamed Ahmed |
| collection | DOAJ |
| description | A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation, thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms. |
| format | Article |
| id | doaj-art-ebf63c39fe7c49a2b7b63ec4583aa8a4 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-ebf63c39fe7c49a2b7b63ec4583aa8a42025-02-02T06:00:36ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-03-0111193310.26599/BDMA.2017.9020002DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix FactorizationNahla Mohamed Ahmed0Ling Chen1Yulong Wang2Bin Li3Yun Li4Wei Liu5<institution content-type="dept">College of Information Engineering</institution>, <institution>Yangzhou University</institution>, <city>Yangzhou</city> <postal-code>225009</postal-code>, <country>China</country>.<institution content-type="dept">College of Information Engineering</institution>, <institution>Yangzhou University</institution>, <city>Yangzhou</city> <postal-code>225009</postal-code>, <country>China</country>.<institution content-type="dept">College of Agriculture</institution>, <institution>Yangzhou University</institution>, <city>Yangzhou</city> <postal-code>225009</postal-code>, <country>China</country>.<institution content-type="dept">College of Information Engineering</institution>, <institution>Yangzhou University</institution>, <city>Yangzhou</city> <postal-code>225009</postal-code>, <country>China</country>.<institution content-type="dept">College of Information Engineering</institution>, <institution>Yangzhou University</institution>, <city>Yangzhou</city> <postal-code>225009</postal-code>, <country>China</country>.<institution content-type="dept">College of Information Engineering</institution>, <institution>Yangzhou University</institution>, <city>Yangzhou</city> <postal-code>225009</postal-code>, <country>China</country>.A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation, thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms.https://www.sciopen.com/article/10.26599/BDMA.2017.9020002dynamic networklink predictionmatrix factorization |
| spellingShingle | Nahla Mohamed Ahmed Ling Chen Yulong Wang Bin Li Yun Li Wei Liu DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization Big Data Mining and Analytics dynamic network link prediction matrix factorization |
| title | DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization |
| title_full | DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization |
| title_fullStr | DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization |
| title_full_unstemmed | DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization |
| title_short | DeepEye: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization |
| title_sort | deepeye link prediction in dynamic networks based on non negative matrix factorization |
| topic | dynamic network link prediction matrix factorization |
| url | https://www.sciopen.com/article/10.26599/BDMA.2017.9020002 |
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