Are all Social Networks Structurally Similar? A Comparative Study using Network Statistics and Metrics

TL;DR

This paper compares various social networks using structural metrics to identify their similarities and differences, revealing that socially diverse networks are structurally similar despite different contexts.

Contribution

It provides a comprehensive analysis of multiple social networks using network statistics, highlighting their structural similarities and differences, and reviews sampling methods.

Findings

Social networks are structurally similar despite different contexts.

Snowball sampling method shows vulnerabilities across network metrics.

Analysis helps understand the fundamental structure of social networks.

Abstract

The modern age has seen an exponential growth of social network data available on the web. Analysis of these networks reveal important structural information about these networks in particular and about our societies in general. More often than not, analysis of these networks is concerned in identifying similarities among social networks and how they are different from other networks such as protein interaction networks, computer networks and food web. In this paper, our objective is to perform a critical analysis of different social networks using structural metrics in an effort to highlight their similarities and differences. We use five different social network datasets which are contextually and semantically different from each other. We then analyze these networks using a number of different network statistics and metrics. Our results show that although these social networks have been constructed from different contexts, they are structurally similar. We also review the snowball sampling method and show its vulnerability against different network metrics.