# A Community-aware Network Growth Model for Synthetic Social Network Generation

## TL;DR

This paper introduces ComAwareNetGrowth, a new community-aware network growth model that effectively replicates key properties of real-world social networks through various link formation mechanisms.

## Contribution

The study presents a novel discrete-time network growth model incorporating multiple link creation mechanisms and community-awareness, improving the realism of synthetic social network generation.

## Key findings

- Model accurately mimics real social network properties
- Capable of generating diverse network structures
- Outperforms existing models in key network metrics

## Abstract

This study proposes a novel network growth model named ComAwareNetGrowth which aims to mimic evolution of real-world social networks. The model works in discrete time. At each timestep, a new link (I) within-community or (II) anywhere in the network is created (a) between existing nodes or (b) between an existing node and a newcoming node, based on (i) random graph model, (ii) preferential attachment model, (iii) a triangle-closing model, or (iv) a quadrangle-closing model. Parameters control the probability of employing a particular mechanism in link creation. Experimental results on Karate and Caltech social networks shows that the model is able to mimic real-word social networks in terms of clustering coefficient, modularity, average path length, diameter, and power law exponent. Further experiments indicate that ComAwareNetGrowth model is able to generate variety of synthetic networks with different statistics.