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International Journal of Computer & Software Engineering Volume 6 (2021), Article ID 6:IJCSE-169, 8 pages
https://doi.org/10.15344/2456-4451/2021/169
Original Article
Special Issue: Computational Analysis and Modeling
Predicting Future Co-researchers Using Relational Graph Convolutional Networks

Hina Watakawa and Tomofumi Matsuzawa*

Department of Information Sciences, Tokyo University of Science, Japan
Prof. Tomofumi Matsuzawa, Department of Information Sciences, Tokyo University of Science, Japan; E-mail: t-matsu@is.noda.tus.ac.jp
05 November 2021; 15 November 2021; 17 November 2021
Watakawa H, Matsuzawa T (2021) Predicting Future Co-Reseachers Using Relational Graph Convolutional Networks. Int J Comput Softw Eng 6: 169. doi: https://doi.org/10.15344/2456-4451/2021/169

Abstract

Finding the right researcher for a joint research project is crucial in improving the outcome of the research. However, owing to the recent popularity of multidisciplinary research, it is difficult to find an appropriate researcher familiar with your field of study. In this study, we constructed a co-authorship network as a heterogeneous graph, using three bibliographic information: author, paper, and field of the paper. In addition, we extracted researchers who may coauthor in the future using a Relational Graph Convolutional Networks (R-GCN).