# Pairwise Link Prediction

## TL;DR

This paper introduces a new pairwise link prediction task focused on predicting the formation of triangles in networks, and develops PageRank-based methods that outperform traditional approaches in various network types.

## Contribution

The paper proposes a novel pairwise link prediction framework targeting triangle formation and extends existing methods with PageRank-based algorithms.

## Key findings

- Diffusion-based methods are more consistent across different network types.
- The pairwise link prediction framework improves standard link prediction accuracy.
- PageRank-based methods outperform traditional approaches in experiments.

## Abstract

Link prediction is a common problem in network science that transects many disciplines. The goal is to forecast the appearance of new links or to find links missing in the network. Typical methods for link prediction use the topology of the network to predict the most likely future or missing connections between a pair of nodes. However, network evolution is often mediated by higher-order structures involving more than pairs of nodes; for example, cliques on three nodes (also called triangles) are key to the structure of social networks, but the standard link prediction framework does not directly predict these structures. To address this gap, we propose a new link prediction task called "pairwise link prediction" that directly targets the prediction of new triangles, where one is tasked with finding which nodes are most likely to form a triangle with a given edge. We develop two PageRank-based methods for our pairwise link prediction problem and make natural extensions to existing link prediction methods. Our experiments on a variety of networks show that diffusion based methods are less sensitive to the type of graphs used and more consistent in their results. We also show how our pairwise link prediction framework can be used to get better predictions within the context of standard link prediction evaluation.