# Making Fast Graph-based Algorithms with Graph Metric Embeddings

## TL;DR

This paper introduces a novel graph embedding method that efficiently approximates node distance measures, enabling fast similarity computations and outperforming existing methods on semantic tasks.

## Contribution

The paper presents a simple, scalable approach for learning graph embeddings that reflect user-defined distance measures, significantly speeding up similarity predictions.

## Key findings

- Achieves several orders of magnitude speed-up in word similarity tasks
- Outperforms existing graph embeddings on semantic similarity and disambiguation
- Effective on WordNet and knowledge base graphs

## Abstract

The computation of distance measures between nodes in graphs is inefficient and does not scale to large graphs. We explore dense vector representations as an effective way to approximate the same information: we introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g.the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks and show evaluations on the WordNet graph and two knowledge base graphs.