# Spring-Electrical Models For Link Prediction

## TL;DR

This paper introduces a novel link prediction method based on spring-electrical models, leveraging network visualization principles to estimate link probabilities across various network types.

## Contribution

It presents a new approach that uses network layout distances from spring-electrical models for link prediction, applicable to multiple network structures.

## Key findings

- Effective in undirected, directed, and bipartite networks
- Outperforms several popular baseline methods
- Demonstrates flexibility and robustness

## Abstract

We propose a link prediction algorithm that is based on spring-electrical models. The idea to study these models came from the fact that spring-electrical models have been successfully used for networks visualization. A good network visualization usually implies that nodes similar in terms of network topology, e.g., connected and/or belonging to one cluster, tend to be visualized close to each other. Therefore, we assumed that the Euclidean distance between nodes in the obtained network layout correlates with a probability of a link between them. We evaluate the proposed method against several popular baselines and demonstrate its flexibility by applying it to undirected, directed and bipartite networks.