# Link Prediction with Mutual Attention for Text-Attributed Networks

arXiv: https://arxiv.org/abs/1902.11054 · HTML: https://mlsanity.com/paper/1902.11054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 2 of 2)

- [Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2104.07917/) (2021)
- [Predicting Links on Wikipedia with Anchor Text Information](https://mlsanity.com/paper/2105.11734/) (2021)

## Abstract (arXiv)

In this extended abstract, we present an algorithm that learns a similarity measure between documents from the network topology of a structured corpus. We leverage the Scaled Dot-Product Attention, a recently proposed attention mechanism, to design a mutual attention mechanism between pairs of documents. To train its parameters, we use the network links as supervision. We provide preliminary experiment results with a citation dataset on two prediction tasks, demonstrating the capacity of our model to learn a meaningful textual similarity.