@inproceedings{4a1ed438902f4e4990aedbf7206715ad,
title = "Link prediction in weighted networks via structural perturbations",
abstract = "Link prediction aims at revealing missing and unknown information from observed network data, or predicting possible evolutions in near future. In recent years, extensive studies of link prediction algorithms have been performed on unweighted networks. However most empirical systems are necessarily to be described as weighted networks rather than solely the topology. In this paper we extend the structural perturbation method to weighted networks. We found that by including weight information the prediction accuracy can be significantly improved on networks with homogeneous weight distributions, meanwhile less improvements for heterogeneous weighted networks. Also we compared the weighted structural perturbation method to some benchmark algorithms, both weighted and unweighted, and found generally better performance in accuracy.",
keywords = "Link prediction, Matrix perturbation, Weighted networks",
author = "Liming Pan and Lei Gao and Jian Gao",
note = "Funding Information: This work is supported in part by China Scholarship Council (CSC) under the Grant CSC No. 201606070053. Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017 ; Conference date: 15-12-2017 Through 17-12-2017",
year = "2017",
month = oct,
day = "20",
doi = "10.1109/ICCWAMTIP.2017.8301417",
language = "English (US)",
series = "2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5--8",
booktitle = "2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2017",
address = "United States",
}