# Link Prediction with Mutual Attention for Text-Attributed Networks

## TL;DR

This paper introduces a mutual attention-based algorithm that learns textual similarity in text-attributed networks using network topology and attention mechanisms, showing promising results on citation datasets.

## Contribution

It proposes a novel mutual attention mechanism leveraging scaled dot-product attention for link prediction in text-attributed networks, trained with network links as supervision.

## Key findings

- Successfully learned meaningful textual similarity
- Demonstrated capacity on citation dataset
- Effective for link prediction tasks

## Abstract

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.