Demographic and Structural Characteristics to Rationalize Link Formation in Online Social Networks

TL;DR

This paper introduces a new network generation model that combines demographic and structural factors to better understand and simulate the formation of links in online social networks, validated on Facebook datasets.

Contribution

The paper presents a novel algorithm that integrates demographic and structural characteristics for modeling social network growth, filling a gap in existing models.

Findings

The model accurately reproduces Facebook network structures.

Incorporating demographic data improves link prediction.

The approach outperforms existing structural-only models.

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 either due to an individual's demographic profile such as age group, gender, geographic location or due to 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 growth of a network.We use different publicly available Facebook datasets as benchmarks to demonstrate the correctness of the proposed network generation model.