# Growth of Common Friends in a Preferential Attachment Model

## TL;DR

This paper analyzes how the number of common friends grows in a preferential attachment model, revealing different growth regimes and providing estimates relevant for social network analysis.

## Contribution

It derives the growth rate of common friends in a linear preferential attachment model and identifies phase transitions in their limiting behavior.

## Key findings

- Growth rate of common friends varies with model parameters
- Identifies power-law, logarithmic, and static growth regimes
- Provides estimates for common friends in social networks

## Abstract

The number of common friends (or connections) in a graph is a commonly used measure of proximity between two nodes. Such measures are used in link prediction algorithms and recommendation systems in large online social networks. We obtain the rate of growth of the number of common friends in a linear preferential attachment model. We apply our result to develop an estimate for the number of common friends. We also observe a phase transition in the limiting behavior of the number of common friends; depending on the range of the parameters of the model, the growth is either power-law, or, logarithmic, or static with the size of the graph.