Entropy-based link prediction in weighted networks
Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks.In a previous work [Xu et al. Physica A, 456 294（2016）], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the con...
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| Published in | Chinese physics B Vol. 26; no. 1; pp. 584 - 590 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-1056 2058-3834 |
| DOI | 10.1088/1674-1056/26/1/018902 |
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| Abstract | Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks.In a previous work [Xu et al. Physica A, 456 294（2016）], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight,and propose a weighted prediction index based on the contributions of paths, namely weighted path entropy（WPE）, to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three other typical weighted indices. |
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| AbstractList | Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks.In a previous work [Xu et al. Physica A, 456 294（2016）], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight,and propose a weighted prediction index based on the contributions of paths, namely weighted path entropy（WPE）, to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three other typical weighted indices. |
| Author | 许忠奇 濮存来 Rajput Ramiz Sharafat 李伦波 杨健 |
| AuthorAffiliation | Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China Department of Industrial and Systems Engineering, University of Florida, Gainesville 32611, USA |
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| CitedBy_id | crossref_primary_10_1016_j_knosys_2022_108402 crossref_primary_10_1016_j_physa_2019_121397 crossref_primary_10_1016_j_knosys_2022_109713 crossref_primary_10_1002_itl2_409 crossref_primary_10_1016_j_physa_2017_04_106 crossref_primary_10_3390_bdcc7010031 crossref_primary_10_7498_aps_69_20191162 crossref_primary_10_1016_j_physa_2018_05_067 crossref_primary_10_1016_j_energy_2022_124684 crossref_primary_10_1016_j_physa_2018_02_189 crossref_primary_10_1016_j_physa_2020_124289 crossref_primary_10_1016_j_egyr_2021_11_270 crossref_primary_10_1016_j_energy_2021_122324 |
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| Notes | link prediction; weighted networks; information entropy Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks.In a previous work [Xu et al. Physica A, 456 294（2016）], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight,and propose a weighted prediction index based on the contributions of paths, namely weighted path entropy（WPE）, to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three other typical weighted indices. Zhongqi Xu1,Cunlai Pu1,2,Rajput Ramiz Sharafat1,Lunbo Li1,Jian Yang1（1. Department of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; 2.Department of Industrial and Systems Engineering, University of Florida, Gainesville 32611, USA） 11-5639/O4 |
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| Title | Entropy-based link prediction in weighted networks |
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