Community analysis in social networks

TL;DR

This paper empirically analyzes social network community structures, revealing power law scaling in community sizes and topological self-similarity, with implications for understanding social network organization.

Contribution

It provides a detailed empirical analysis of community structures in social networks, including scaling properties and self-similarity, using visualisation and quantitative measures.

Findings

Community size distributions follow power law scaling with exponents -0.5 or -1.

Networks exhibit topological self-similarity in community structure.

A visualising technique helps illustrate community organization.

Abstract

We present an empirical study of different social networks obtained from digital repositories. Our analysis reveals the community structure and provides a useful visualising technique. We investigate the scaling properties of the community size distribution, and that find all the networks exhibit power law scaling in the community size distributions with exponent either -0.5 or -1. Finally we find that the networks' community structure is topologically self-similar using the Horton-Strahler index.