# Link Prediction using Top-$k$ Shortest Distances

## TL;DR

This paper introduces a top-$k$ shortest distance algorithm for link prediction, demonstrating it outperforms traditional similarity measures like Jaccard and Adamic/Adar in accuracy.

## Contribution

The paper presents a novel application of top-$k$ shortest distances for link prediction, showing improved performance over existing methods.

## Key findings

- Top-$k$ distances outperform classical similarity measures
- The method is efficient and effective for link prediction
- Results are validated against baseline and state-of-the-art methods

## Abstract

In this paper, we apply an efficient top-$k$ shortest distance routing algorithm to the link prediction problem and test its efficacy. We compare the results with other base line and state-of-the-art methods as well as with the shortest path. Our results show that using top-$k$ distances as a similarity measure outperforms classical similarity measures such as Jaccard and Adamic/Adar.