Predicting Missing Links Based on a New Triangle Structure
With the rapid growth of various complex networks, link prediction has become increasingly important because it can discover the missing information and predict future interactions between nodes in a network. Recently, the CAR and CCLP indexes have been presented for link prediction by means of diff...
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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/10.1155/2018/7312603 |
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| _version_ | 1832562536331870208 |
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| author | Shenshen Bai Longjie Li Jianjun Cheng Shijin Xu Xiaoyun Chen |
| author_facet | Shenshen Bai Longjie Li Jianjun Cheng Shijin Xu Xiaoyun Chen |
| author_sort | Shenshen Bai |
| collection | DOAJ |
| description | With the rapid growth of various complex networks, link prediction has become increasingly important because it can discover the missing information and predict future interactions between nodes in a network. Recently, the CAR and CCLP indexes have been presented for link prediction by means of different triangle structure information. However, both indexes may lose the contributions of some shared neighbors. We propose in this work a new index to make up the weakness and then improve the accuracy of link prediction. The proposed index focuses on a new triangle structure, i.e., the triangle formed by one seed node, one common neighbor, and one other node. It emphasizes the importance of these triangles but does not ignore the contribution of any common neighbor. In addition, the proposed index adopts the theory of resource allocation by penalizing large-degree neighbors. The results of comparison with CN, AA, RA, ADP, CAR, CAA, CRA, and CCLP on 12 real-world networks show that the proposed index outperforms the compared methods in terms of AUC and ranking score. |
| format | Article |
| id | doaj-art-7156ca7292b34acf885bd4176a036fdc |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7156ca7292b34acf885bd4176a036fdc2025-02-03T01:22:26ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/73126037312603Predicting Missing Links Based on a New Triangle StructureShenshen Bai0Longjie Li1Jianjun Cheng2Shijin Xu3Xiaoyun Chen4School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science & Engineering, Lanzhou University, Lanzhou 730000, ChinaWith the rapid growth of various complex networks, link prediction has become increasingly important because it can discover the missing information and predict future interactions between nodes in a network. Recently, the CAR and CCLP indexes have been presented for link prediction by means of different triangle structure information. However, both indexes may lose the contributions of some shared neighbors. We propose in this work a new index to make up the weakness and then improve the accuracy of link prediction. The proposed index focuses on a new triangle structure, i.e., the triangle formed by one seed node, one common neighbor, and one other node. It emphasizes the importance of these triangles but does not ignore the contribution of any common neighbor. In addition, the proposed index adopts the theory of resource allocation by penalizing large-degree neighbors. The results of comparison with CN, AA, RA, ADP, CAR, CAA, CRA, and CCLP on 12 real-world networks show that the proposed index outperforms the compared methods in terms of AUC and ranking score.http://dx.doi.org/10.1155/2018/7312603 |
| spellingShingle | Shenshen Bai Longjie Li Jianjun Cheng Shijin Xu Xiaoyun Chen Predicting Missing Links Based on a New Triangle Structure Complexity |
| title | Predicting Missing Links Based on a New Triangle Structure |
| title_full | Predicting Missing Links Based on a New Triangle Structure |
| title_fullStr | Predicting Missing Links Based on a New Triangle Structure |
| title_full_unstemmed | Predicting Missing Links Based on a New Triangle Structure |
| title_short | Predicting Missing Links Based on a New Triangle Structure |
| title_sort | predicting missing links based on a new triangle structure |
| url | http://dx.doi.org/10.1155/2018/7312603 |
| work_keys_str_mv | AT shenshenbai predictingmissinglinksbasedonanewtrianglestructure AT longjieli predictingmissinglinksbasedonanewtrianglestructure AT jianjuncheng predictingmissinglinksbasedonanewtrianglestructure AT shijinxu predictingmissinglinksbasedonanewtrianglestructure AT xiaoyunchen predictingmissinglinksbasedonanewtrianglestructure |