Truncated Graph-Regularized Low Rank Representation for Link Prediction
Link prediction, whose primitive aim lies in remodeling or inferring link formations in complex networks, has been accepted as a fundamental study in understanding interactions between specific node pairs. To overcome the shortcomings of sparsity and intricacy in networks, an iterative method is pro...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8684231/ |
| _version_ | 1818323555177201664 |
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| author | Cuiqi Si Licheng Jiao Jianshe Wu |
| author_facet | Cuiqi Si Licheng Jiao Jianshe Wu |
| author_sort | Cuiqi Si |
| collection | DOAJ |
| description | Link prediction, whose primitive aim lies in remodeling or inferring link formations in complex networks, has been accepted as a fundamental study in understanding interactions between specific node pairs. To overcome the shortcomings of sparsity and intricacy in networks, an iterative method is proposed to comprehensively describe links in both local and global perspectives. From defining a truncated similarity matrix, local inherited properties in the network are maintained. With the refined similarity, a graph-regularized low-rank representation is provided to simultaneously preserve local and global structure information. Then, the representation is optimized to accurately predict link interactions in the network. Compared with the state-of-arts on real-world networks, the competitive experimental results demonstrate that our method is capable of effectively delineating interactions in multiple networks. |
| first_indexed | 2024-12-13T11:14:33Z |
| format | Article |
| id | doaj.art-cb55c9a991ba448a9870437371fca8e9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:14:33Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cb55c9a991ba448a9870437371fca8e92022-12-21T23:48:39ZengIEEEIEEE Access2169-35362019-01-017482244823510.1109/ACCESS.2019.29097578684231Truncated Graph-Regularized Low Rank Representation for Link PredictionCuiqi Si0https://orcid.org/0000-0003-0354-6781Licheng Jiao1Jianshe Wu2Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, ChinaKey Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, ChinaLink prediction, whose primitive aim lies in remodeling or inferring link formations in complex networks, has been accepted as a fundamental study in understanding interactions between specific node pairs. To overcome the shortcomings of sparsity and intricacy in networks, an iterative method is proposed to comprehensively describe links in both local and global perspectives. From defining a truncated similarity matrix, local inherited properties in the network are maintained. With the refined similarity, a graph-regularized low-rank representation is provided to simultaneously preserve local and global structure information. Then, the representation is optimized to accurately predict link interactions in the network. Compared with the state-of-arts on real-world networks, the competitive experimental results demonstrate that our method is capable of effectively delineating interactions in multiple networks.https://ieeexplore.ieee.org/document/8684231/Link predictionlow rank representationmanifold regularizationtruncated similarity |
| spellingShingle | Cuiqi Si Licheng Jiao Jianshe Wu Truncated Graph-Regularized Low Rank Representation for Link Prediction IEEE Access Link prediction low rank representation manifold regularization truncated similarity |
| title | Truncated Graph-Regularized Low Rank Representation for Link Prediction |
| title_full | Truncated Graph-Regularized Low Rank Representation for Link Prediction |
| title_fullStr | Truncated Graph-Regularized Low Rank Representation for Link Prediction |
| title_full_unstemmed | Truncated Graph-Regularized Low Rank Representation for Link Prediction |
| title_short | Truncated Graph-Regularized Low Rank Representation for Link Prediction |
| title_sort | truncated graph regularized low rank representation for link prediction |
| topic | Link prediction low rank representation manifold regularization truncated similarity |
| url | https://ieeexplore.ieee.org/document/8684231/ |
| work_keys_str_mv | AT cuiqisi truncatedgraphregularizedlowrankrepresentationforlinkprediction AT lichengjiao truncatedgraphregularizedlowrankrepresentationforlinkprediction AT jianshewu truncatedgraphregularizedlowrankrepresentationforlinkprediction |