Enriching Egocentric Co-authorship Networks with Survey Data: A Weighted Network Analysis of Academic Collaboration

Scientific collaboration extends well beyond co-authorship. However, most network-based studies rely exclusively on bibliographic data. This paper proposes a framework for constructing and analysing enriched egocentric collaboration networks by integrating Scopus publication records with original survey data collected among Italian academics in Statistics, Management, and Sociology. For each scholar, we build a weighted ego-network characterized by multiple tie-strength dimensions and by formal and informal antecedents of tie formation. The broader collaboration network is further extended to non-co-authorship activities across four domains. We identify the typologies of the ego-network using clustering methods, while the network structure and composition are described through multivariate indices and correspondence analysis.