Connecting Patterns Inspire Link Prediction in Complex Networks
Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/8581365 |
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| _version_ | 1832561669589434368 |
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| author | Ming-Yang Zhou Hao Liao Wen-Man Xiong Xiang-Yang Wu Zong-Wen Wei |
| author_facet | Ming-Yang Zhou Hao Liao Wen-Man Xiong Xiang-Yang Wu Zong-Wen Wei |
| author_sort | Ming-Yang Zhou |
| collection | DOAJ |
| description | Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks. |
| format | Article |
| id | doaj-art-df3069f34f77410cb6375ac8e7ef14b7 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-df3069f34f77410cb6375ac8e7ef14b72025-02-03T01:24:30ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/85813658581365Connecting Patterns Inspire Link Prediction in Complex NetworksMing-Yang Zhou0Hao Liao1Wen-Man Xiong2Xiang-Yang Wu3Zong-Wen Wei4Guangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Province Key Laboratory of Popular High Performance Computers, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, ChinaLink prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they share many common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks.http://dx.doi.org/10.1155/2017/8581365 |
| spellingShingle | Ming-Yang Zhou Hao Liao Wen-Man Xiong Xiang-Yang Wu Zong-Wen Wei Connecting Patterns Inspire Link Prediction in Complex Networks Complexity |
| title | Connecting Patterns Inspire Link Prediction in Complex Networks |
| title_full | Connecting Patterns Inspire Link Prediction in Complex Networks |
| title_fullStr | Connecting Patterns Inspire Link Prediction in Complex Networks |
| title_full_unstemmed | Connecting Patterns Inspire Link Prediction in Complex Networks |
| title_short | Connecting Patterns Inspire Link Prediction in Complex Networks |
| title_sort | connecting patterns inspire link prediction in complex networks |
| url | http://dx.doi.org/10.1155/2017/8581365 |
| work_keys_str_mv | AT mingyangzhou connectingpatternsinspirelinkpredictionincomplexnetworks AT haoliao connectingpatternsinspirelinkpredictionincomplexnetworks AT wenmanxiong connectingpatternsinspirelinkpredictionincomplexnetworks AT xiangyangwu connectingpatternsinspirelinkpredictionincomplexnetworks AT zongwenwei connectingpatternsinspirelinkpredictionincomplexnetworks |