Version 1
: Received: 22 November 2022 / Approved: 23 November 2022 / Online: 23 November 2022 (07:38:22 CET)
How to cite:
Huq, M.R.; Jennifer, S.S.; Partho, S.M.; Fairuz, F. A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models. Preprints2022, 2022110439. https://doi.org/10.20944/preprints202211.0439.v1
Huq, M.R.; Jennifer, S.S.; Partho, S.M.; Fairuz, F. A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models. Preprints 2022, 2022110439. https://doi.org/10.20944/preprints202211.0439.v1
Huq, M.R.; Jennifer, S.S.; Partho, S.M.; Fairuz, F. A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models. Preprints2022, 2022110439. https://doi.org/10.20944/preprints202211.0439.v1
APA Style
Huq, M.R., Jennifer, S.S., Partho, S.M., & Fairuz, F. (2022). A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models. Preprints. https://doi.org/10.20944/preprints202211.0439.v1
Chicago/Turabian Style
Huq, M.R., Shafiul Mahmud Partho and Fariha Fairuz. 2022 "A Comparative Study between Graph Database and Traditional Approach to forecast Coauthor Link Prediction based on Machine Learning Models" Preprints. https://doi.org/10.20944/preprints202211.0439.v1
Abstract
In the modern world where research is taking a huge leap, the collaboration network between authors is also expanding, increasing the probability of different authors coming together to work on the same project, same research paper making them co-authors. In coauthorship, link prediction is used to anticipate new interactions between its members that are likely to occur in the future. Researchers have concentrated their efforts on studying and suggesting methods for providing effective reviews for authors who can collaborate on a scientific endeavor. In order to provide a precise link prediction, a graph database approach is proposed in this paper using nodes to determine most possible co-authors in future. In order to forecast the connections, we preprocessed the data set for the maximum relative contents. A supervised learning approach is used to execute the solution, which includes random forest classifier and logistic regression. The first findings of our technique reveal that the total of two author node’s research collaboration indices has the greatest influence on the performance of supervised link prediction than that of the traditional approach, which stimulates us to conduct further study on employing such a forecast.
Keywords
coauthorship; coauthorship Network; Link Prediction; Graph Database; Nodes
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
