# Generating online social networks based on socio-demographic attributes

## TL;DR

This paper introduces a new social network generation model that combines demographic attributes and structural dynamics to better simulate real-world online social networks like Facebook.

## Contribution

It presents a novel algorithm that integrates demographic and structural factors in network formation, addressing limitations of previous models.

## Key findings

- The model accurately reproduces Facebook network properties.
- It can generate networks with diverse demographic and structural features.
- Demonstrated effectiveness on multiple publicly available datasets.

## Abstract

Recent years have seen tremendous growth of many online social networks such as Facebook, LinkedIn and MySpace. People connect to each other through these networks forming large social communities providing researchers rich datasets to understand, model and predict social interactions and behaviors. New contacts in these networks can be formed due to an individual's demographic attributes such as age group, gender, geographic location, or due to a network's structural dynamics such as triadic closure and preferential attachment, or a combination of both demographic and structural characteristics. A number of network generation models have been proposed in the last decade to explain the structure, evolution and processes taking place in different types of networks, and notably social networks. Network generation models studied in the literature primarily consider structural properties, and in some cases an individual's demographic profile in the formation of new social contacts. These models do not present a mechanism to combine both structural and demographic characteristics for the formation of new links. In this paper, we propose a new network generation algorithm which incorporates both these characteristics to model network formation. We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model. The proposed model is flexible and thus can generate networks with varying demographic and structural properties.