Article Dans Une Revue International Journal of Human-Computer Interaction Année : 2025

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

Résumé

Collaboration networks are widely studied in both natural and social sciences as they reveal collaboration patterns among individuals and institutions. For example, coauthorship networks, which map collaborations between authors, are particularly useful for analyzing influential collaborators and understanding the development and dissemination of knowledge in specific research fields. However, data on collaborations are often complex, involving heterogeneous entities distributed over time, and are typically represented as multiple interconnected nodes. This complexity can 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. This technique allows users to concentrate on specific data points while navigating through multiple layers of information. We utilize 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.

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hal-05302463 , version 1 (14-10-2025)

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Aline Menin, Paolo Buono, Marco Winckler. Muvin: A Visual Analytics Tool for Exploring Dynamic Collaboration Networks from Knowledge Graphs. International Journal of Human-Computer Interaction, 2025, ⟨10.1080/10447318.2025.2581255⟩. ⟨hal-05302463⟩
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