Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method
Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity m...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/3579758 |
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| _version_ | 1850228205450952704 |
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| author | Longjie Li Shenshen Bai Mingwei Leng Lu Wang Xiaoyun Chen |
| author_facet | Longjie Li Shenshen Bai Mingwei Leng Lu Wang Xiaoyun Chen |
| author_sort | Longjie Li |
| collection | DOAJ |
| description | Link prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods. |
| format | Article |
| id | doaj-art-228e03e2ed2a43609319cb67ad0ba815 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-228e03e2ed2a43609319cb67ad0ba8152025-08-20T02:04:36ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/35797583579758Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making MethodLongjie Li0Shenshen Bai1Mingwei Leng2Lu Wang3Xiaoyun Chen4School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaCollege of Educational Science and Technology, Northwest Minzu University, Lanzhou 730030, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaLink prediction, which aims to forecast potential or missing links in a complex network based on currently observed information, has drawn growing attention from researchers. To date, a host of similarity-based methods have been put forward. Usually, one method harbors the idea that one similarity measure is applicable to various networks, and thus has performance fluctuation on different networks. In this paper, we propose a novel method to solve this issue by regarding link prediction as a multiple-attribute decision-making (MADM) problem. In the proposed method, we consider RA, LP, and CAR indices as the multiattribute for node pairs. The technique for order performance by similarity to ideal solution (TOPSIS) is adopted to aggregate the multiattribute and rank node pairs. The proposed method is not limited to only one similarity measure, but takes separate measures into account, since different networks may have different topological structures. Experimental results on 10 real-world networks manifest that the proposed method is superior in comparison to state-of-the-art methods.http://dx.doi.org/10.1155/2018/3579758 |
| spellingShingle | Longjie Li Shenshen Bai Mingwei Leng Lu Wang Xiaoyun Chen Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method Complexity |
| title | Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method |
| title_full | Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method |
| title_fullStr | Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method |
| title_full_unstemmed | Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method |
| title_short | Finding Missing Links in Complex Networks: A Multiple-Attribute Decision-Making Method |
| title_sort | finding missing links in complex networks a multiple attribute decision making method |
| url | http://dx.doi.org/10.1155/2018/3579758 |
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