Citation network applications in a scientific co-authorship recommender system
Digital Libraries
2021-12-01 v1 Machine Learning
Abstract
The problem of co-authors selection in the area of scientific collaborations might be a daunting one. In this paper, we propose a new pipeline that effectively utilizes citation data in the link prediction task on the co-authorship network. In particular, we explore the capabilities of a recommender system based on data aggregation strategies on different graphs. Since graph neural networks proved their efficiency on a wide range of tasks related to recommendation systems, we leverage them as a relevant method for the forecasting of potential collaborations in the scientific community.
Cite
@article{arxiv.2111.15466,
title = {Citation network applications in a scientific co-authorship recommender system},
author = {Vladislav Tishin and Artyom Sosedka and Peter Ibragimov and Vadim Porvatov},
journal= {arXiv preprint arXiv:2111.15466},
year = {2021}
}
Comments
7 pages