# Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors

## TL;DR

This paper introduces a novel network embedding method that jointly encodes network structure and textual node descriptions using recurrent neural encoders, improving link prediction in biomedical networks.

## Contribution

It extends NODE2VEC to incorporate textual node descriptors, combining structural and textual information for enhanced network embedding.

## Key findings

- Outperforms previous methods in link prediction tasks
- Effectively integrates network structure and text data
- Demonstrates improved embedding quality in biomedical networks

## Abstract

Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.