# Citation network applications in a scientific co-authorship recommender system

arXiv: https://arxiv.org/abs/2111.15466 · HTML: https://mlsanity.com/paper/2111.15466/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Edge Proposal Sets for Link Prediction](https://mlsanity.com/paper/2106.15810/) (2021)
- [Attributed Network Embedding via Subspace Discovery](https://mlsanity.com/paper/1901.04095/) (2019)
- [Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracy](https://mlsanity.com/paper/2108.10108/) (2021)

## 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.