# Hidden space reconstruction inspires link prediction in complex networks

## TL;DR

This paper introduces a hidden space reconstruction method that maps networks into Euclidean space, improving link prediction accuracy by using node distances as a similarity metric and combining it with existing methods.

## Contribution

The study presents a novel hidden space mapping approach that enhances link prediction by aligning reconstructed locations with real ones and integrating with current similarity measures.

## Key findings

- Reconstructed hidden space locations align with real geographical positions.
- Hidden space distances outperform traditional similarity indices in link prediction.
- Hybrid models combining hidden space with other methods significantly improve accuracy.

## Abstract

As a fundamental challenge in vast disciplines, link prediction aims to identify potential links in a network based on the incomplete observed information, which has broad applications ranging from uncovering missing protein-protein interaction to predicting the evolution of networks. One of the most influential methods rely on similarity indices characterized by the common neighbors or its variations. We construct a hidden space mapping a network into Euclidean space based solely on the connection structures of a network. Compared with real geographical locations of nodes, our reconstructed locations are in conformity with those real ones. The distances between nodes in our hidden space could serve as a novel similarity metric in link prediction. In addition, we hybrid our hidden space method with other state-of-the-art similarity methods which substantially outperforms the existing methods on the prediction accuracy. Hence, our hidden space reconstruction model provides a fresh perspective to understand the network structure, which in particular casts a new light on link prediction.