Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks
Low rank matrices approximations have been used in link prediction for networks, which are usually global optimal methods and lack of using the local information. The block structure is a significant local feature of matrices: entities in the same block have similar values, which implies that links...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/786156 |
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| author | Enming Dong Jianping Li Zheng Xie |
| author_facet | Enming Dong Jianping Li Zheng Xie |
| author_sort | Enming Dong |
| collection | DOAJ |
| description | Low rank matrices approximations have been used in link prediction for networks, which are usually global optimal methods and lack of using the local information. The block structure is a significant local feature of matrices: entities in the same block have similar values, which implies that links are more likely to be found within dense blocks. We use this insight to give a probabilistic latent variable model for finding missing links by convex nonnegative matrix factorization with block detection. The experiments show that this method gives better prediction accuracy than original method alone. Different from the original low rank matrices approximations methods for link prediction, the sparseness of solutions is in accord with the sparse property for most real complex networks. Scaling to massive size network, we use the block information mapping matrices onto distributed architectures and give a divide-and-conquer prediction method. The experiments show that it gives better results than common neighbors method when the networks have a large number of missing links. |
| format | Article |
| id | doaj-art-0f919f1a92994a5cb1d8a8e65fa8f72b |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-0f919f1a92994a5cb1d8a8e65fa8f72b2025-02-03T05:46:19ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/786156786156Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale BlocksEnming Dong0Jianping Li1Zheng Xie2College of Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Science, National University of Defense Technology, Changsha 410073, ChinaCollege of Science, National University of Defense Technology, Changsha 410073, ChinaLow rank matrices approximations have been used in link prediction for networks, which are usually global optimal methods and lack of using the local information. The block structure is a significant local feature of matrices: entities in the same block have similar values, which implies that links are more likely to be found within dense blocks. We use this insight to give a probabilistic latent variable model for finding missing links by convex nonnegative matrix factorization with block detection. The experiments show that this method gives better prediction accuracy than original method alone. Different from the original low rank matrices approximations methods for link prediction, the sparseness of solutions is in accord with the sparse property for most real complex networks. Scaling to massive size network, we use the block information mapping matrices onto distributed architectures and give a divide-and-conquer prediction method. The experiments show that it gives better results than common neighbors method when the networks have a large number of missing links.http://dx.doi.org/10.1155/2014/786156 |
| spellingShingle | Enming Dong Jianping Li Zheng Xie Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks Journal of Applied Mathematics |
| title | Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks |
| title_full | Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks |
| title_fullStr | Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks |
| title_full_unstemmed | Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks |
| title_short | Link Prediction via Convex Nonnegative Matrix Factorization on Multiscale Blocks |
| title_sort | link prediction via convex nonnegative matrix factorization on multiscale blocks |
| url | http://dx.doi.org/10.1155/2014/786156 |
| work_keys_str_mv | AT enmingdong linkpredictionviaconvexnonnegativematrixfactorizationonmultiscaleblocks AT jianpingli linkpredictionviaconvexnonnegativematrixfactorizationonmultiscaleblocks AT zhengxie linkpredictionviaconvexnonnegativematrixfactorizationonmultiscaleblocks |