Business networks are a key driver of innovation and economic growth. However, a major challenge is how to discover these network relationships in heterogeneous data sources. In this paper, we present an IT artifact that unifies different data types, including patent, research funding, and publication information, into a unified graph database. This allows a comprehensive analysis of cooperation patterns. Community detection algorithms are used to identify research clusters, while centrality measures reveal key players. Visualizations facilitate the interpretation of research results and provide a user-friendly way to display data about research communities and institutional behavior. A prototype visualization of these results provides a proof of concept for the practicality of the method. The proposed design provides a robust framework for understanding the dynamics of collaborative networks.     «

Business networks are a key driver of innovation and economic growth. However, a major challenge is how to discover these network relationships in heterogeneous data sources. In this paper, we present an IT artifact that unifies different data types, including patent, research funding, and publication information, into a unified graph database. This allows a comprehensive analysis of cooperation patterns. Community detection algorithms are used to identify research clusters, while centrality mea...     »