Clusters, Graphs, and Networks for Analysing Internet-Web-Supported Communication within a Virtual Community

TL;DR

This paper employs clustering, graph theory, and social network analysis to quantitatively examine the structure of European academic Web sites as a virtual community, revealing insights into their connectivity and organization.

Contribution

It introduces a combined approach using clusters, graphs, and networks to analyze Web structure, specifically applied to European academic sites as a social network.

Findings

Web sites form identifiable clusters

Network analysis reveals structural properties of the community

Social network metrics applied to Web data

Abstract

The proposal is to use clusters, graphs and networks as models in order to analyse the Web structure. Clusters, graphs and networks provide knowledge representation and organization. Clusters were generated by co-site analysis. The sample is a set of academic Web sites from the countries belonging to the European Union. These clusters are here revisited from the point of view of graph theory and social network analysis. This is a quantitative and structural analysis. In fact, the Internet is a computer network that connects people and organizations. Thus we may consider it to be a social network. The set of Web academic sites represents an empirical social network, and is viewed as a virtual community. The network structural properties are here analysed applying together cluster analysis, graph theory and social network analysis.