Mapping professional networks at the intersection of culture and technology

Abstract

This thesis explores how hidden professional relationships, referred to as latent ties, can be inferred from publicly available LinkedIn profiles when explicit social connections are missing. Focusing on members of the CultTech Association, the study uses automated data scraping to enrich profile data and applies unsupervised text clustering to categorize individuals by sector. A professional network is then constructed based on shared attributes such as company, school, country, and sector. Network analysis reveals a small-world structure with high clustering and identifiable communities, along with central actors who are potentially influential. The methodology provides a reproducible framework for inferring and analyzing professional connections in contexts where direct relationship data is unavailable.