Applying Social Network Analysis to Analyze a Web-Based Community

TL;DR

This paper applies social network analysis to a web-based community, specifically Book-Crossing, to infer user relations and preferences through measures like degree centrality and affiliation network analysis.

Contribution

It introduces a novel application of social network analysis techniques to infer social relations and user behavior in an online book community.

Findings

Identified key users and popular books using centrality measures.

Inferred potential social relations among users based on shared interests.

Demonstrated the effectiveness of affiliation network analysis in community analysis.

Abstract

This paper deals with a very renowned website (that is Book-Crossing) from two angles: The first angle focuses on the direct relations between users and books. Many things can be inferred from this part of analysis such as who is more interested in book reading than others and why? Which books are most popular and which users are most active and why? The task requires the use of certain social network analysis measures (e.g. degree centrality). What does it mean when two users like the same book? Is it the same when other two users have one thousand books in common? Who is more likely to be a friend of whom and why? Are there specific people in the community who are more qualified to establish large circles of social relations? These questions (and of course others) were answered through the other part of the analysis, which will take us to probe the potential social relations between users in this community. Although these relationships do not exist explicitly, they can be inferred with the help of affiliation network analysis and techniques such as m-slice.