Muvin: A Visual Analytics Tool for Exploring Dynamic Collaboration Networks from Knowledge Graphs

Visualizing collaboration networks supports studies in both natural and social sciences as they reveal collaboration patterns among individuals and institutions. However, the complexity of data, involving heterogeneous timely and interconnected entities, often lead to visual clutter, making it challenging to create effective visualizations. This paper explores the use of an incremental approach to facilitate the exploration of co-authorship networks composed of multivariate entities distributed over time. We demonstrate how incremental visualization can assist users in focusing on relevant data while addressing scalability issues through a focus+context technique. We use a tool called Muvin, which implements this incremental approach to explore multi-sourced linked open data (LOD). Although Muvin can be applied to various types of collaboration networks, this study focuses specifically on co-authorship networks. A user study involving 19 participants offers insights into how the incremental approach supports domain-specific tasks using co-authorship networks.