A link prediction method based on compressed sensing for social networks
Author:
Funder
the innovative talents project for doctoral students of Chongqing University of Posts and Telecommunications
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05060-y.pdf
Reference67 articles.
1. Zhou T (2021) Progresses and challenges in link prediction. iScience 24(11):103217. https://doi.org/10.1016/j.isci.2021.103217
2. Rahman MS, Dey LR, Haider S, Uddin MA, Islam M (2017) Link prediction by correlation on social network. In 20th International Conference of Computer and Information Technology (ICCIT), Dhaka, Bangladesh, pp 1–6. https://doi.org/10.1109/ICCITECHN.2017.8281812
3. Makarov I, Gerasimova O, Sulimov P, Zhukov LE (2019) Dual network embedding for representing research interests in the link prediction problem on co-authorship networks’. PeerJ Comput Sci 5:e172. https://doi.org/10.7717/peerj-cs.172
4. Sharma PK, Rathore S, Park JH (2019) Multilevel learning based modeling for link prediction and users’ consumption preference in Online Social Networks. Futur Gener Comput Syst 93:952–961. https://doi.org/10.1016/j.future.2017.08.031
5. Han Z et al (2023) An attention-based representation learning model for multiple relational knowledge graph. Expert Syst. https://doi.org/10.1111/EXSY.13234
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