Citation network applications in a scientific co-authorship recommender system
This addresses the challenge of finding collaborators for researchers, but it appears incremental as it applies existing methods to a specific domain.
The paper tackled the problem of selecting co-authors in scientific collaborations by proposing a pipeline that uses citation data for link prediction on co-authorship networks, resulting in an effective recommender system based on graph neural networks.
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.