Link prediction in co-authorship networks based on hybrid content similarity metric
Article
Article Title | Link prediction in co-authorship networks based on hybrid |
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ERA Journal ID | 17757 |
Article Category | Article |
Authors | Chuan, Pham Minh (Author), Son, Le Hoang (Author), Ali, Mumtaz (Author), Khang, Tran Dinh (Author), Huong, Le Thanh (Author) and Dey, Nilanjan (Author) |
Journal Title | Applied Intelligence |
Journal Citation | 48 (8), pp. 2470-2486 |
Number of Pages | 17 |
Year | 2018 |
Publisher | Springer |
Place of Publication | United States |
ISSN | 0924-669X |
1573-7497 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s10489-017-1086-x |
Web Address (URL) | https://link.springer.com/article/10.1007%2Fs10489-017-1086-x |
Abstract | Link prediction in online social networks is used to determine new interactions among its members which are likely to occur in the future. Link prediction in the coauthorship network has been regarded as one of the main targets in link prediction researches so far. Researchers have focused on analyzing and proposing solutions to give efficient recommendation for authors who can work together in a science project. In order to give precise prediction of links between two ubiquitous authors in a co-authorship network, it is preferable to design a similarity metric between them and then utilizing it to determine the most possible co-author(s). However, the relevant researches did not regard the integration of paper’s content in the metric itself. This is important when considering the collaboration between scientists since it is possible that authors having same research interests are more likely to have a joint paper than those in different researches. In this paper, we propose a new metric for link prediction in the coauthorship network based on the content similarity named as LDAcosin. Mathematical notions of the link prediction in the co-authorship network and a link prediction algorithm based on topic modeling are proposed. The new metric is experimentally validated on the public bibliographic collection. |
Keywords | link prediction, co-authorship networks, network topology, LDA, topic modeling |
ANZSRC Field of Research 2020 | 499999. Other mathematical sciences not elsewhere classified |
490101. Approximation theory and asymptotic methods | |
Byline Affiliations | Hung Yen University of Technology and Education, Vietnam |
Vietnam National University, Vietnam | |
School of Agricultural, Computational and Environmental Sciences | |
Hanoi University of Science and Technology, Vietnam | |
Techno International Newtown, India | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q48yz/link-prediction-in-co-authorship-networks-based-on-hybrid-content-similarity-metric
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