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

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Title: Muvin: A Visual Analytics Tool for Exploring Dynamic Collaboration Networks from Knowledge Graphs.
Authors: Menin, Aline (AUTHOR), Buono, Paolo (AUTHOR), Winckler, Marco (AUTHOR)
Source: International Journal of Human-Computer Interaction. Jul2026, Vol. 42 Issue 13, p10053-10077. 25p.
Subjects: Visual analytics, Knowledge graphs, Cooperative research, Linked data (Semantic Web), User-centered system design, Visualization, Business networks
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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PubType: Academic Journal
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  Data: Muvin: A Visual Analytics Tool for Exploring Dynamic Collaboration Networks from Knowledge Graphs.
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  Data: <searchLink fieldCode="AR" term="%22Menin%2C+Aline%22">Menin, Aline</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Buono%2C+Paolo%22">Buono, Paolo</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Winckler%2C+Marco%22">Winckler, Marco</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Jul2026, Vol. 42 Issue 13, p10053-10077. 25p.
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  Data: <searchLink fieldCode="DE" term="%22Visual+analytics%22">Visual analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+graphs%22">Knowledge graphs</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+research%22">Cooperative research</searchLink><br /><searchLink fieldCode="DE" term="%22Linked+data+%28Semantic+Web%29%22">Linked data (Semantic Web)</searchLink><br /><searchLink fieldCode="DE" term="%22User-centered+system+design%22">User-centered system design</searchLink><br /><searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Business+networks%22">Business networks</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=194842598
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        Value: 10.1080/10447318.2025.2581255
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Knowledge graphs
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      – SubjectFull: Cooperative research
        Type: general
      – SubjectFull: Linked data (Semantic Web)
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      – SubjectFull: User-centered system design
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      – SubjectFull: Visualization
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      – SubjectFull: Business networks
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              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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