Citation network applications in a scientific co-authorship recommender system
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Abstract (arXiv)
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.
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References · 8
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
- 2018Link Prediction Based on Graph Neural Networkscited by 829
- 2021Edge Proposal Sets for Link Predictioncited by 16
- 2019Attributed Network Embedding via Subspace Discoverycited by 11
- 2021Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracycited by 2
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