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CMGV: Algorithms and a unified framework for complexity management in graph visualization.

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Τίτλος: CMGV: Algorithms and a unified framework for complexity management in graph visualization.
Συγγραφείς: Zafar, Osama1 (AUTHOR), Dogrusoz, Ugur2 (AUTHOR) ugur@cs.bilkent.edu.tr, Balci, Hasan3 (AUTHOR), Halac, Ahmet Feyzi2 (AUTHOR)
Πηγή: Information Visualization. Apr2026, Vol. 25 Issue 2, p192-208. 17p.
Θεματικοί όροι: Data visualization, Algorithms, Visualization, Computational complexity, Mental representation, Graph theory
Περίληψη: Illustrating data visually through graphs enables the examination of valuable insights and the identification of crucial patterns that are beneficial for the user. However, as the volume of data grows, it becomes increasingly challenging to organize corresponding graphs and zero in on specific objects and/or relations of interest. Various techniques have been developed to tackle the complexity of large graphs, yet these methods operate independently, potentially resulting in inconsistencies and incorrect behaviors, as well as inefficient use of computing resources when mixed together and applied in a certain order. Furthermore, administering these methods can lead to significant changes in the layout of the graph, potentially disorienting the user and disrupting their mental map. This study aims to design a framework and associated algorithms for efficiently and effectively managing complexity during visual analysis of large relational data represented as graphs, by seamlessly integrating various complexity management techniques and making adjustments to the graph layout after each operation to preserve the user's mental map. A rendering-independent implementation of this framework and associated algorithms, as well as its integration with a popular graph rendering library named Cytoscape.js, can be accessed freely on GitHub. [ABSTRACT FROM AUTHOR]
Βάση Δεδομένων: Supplemental Index
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PubType: Academic Journal
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  Data: CMGV: Algorithms and a unified framework for complexity management in graph visualization.
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  Data: <searchLink fieldCode="AR" term="%22Zafar%2C+Osama%22">Zafar, Osama</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dogrusoz%2C+Ugur%22">Dogrusoz, Ugur</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> ugur@cs.bilkent.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Balci%2C+Hasan%22">Balci, Hasan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Halac%2C+Ahmet+Feyzi%22">Halac, Ahmet Feyzi</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Information+Visualization%22">Information Visualization</searchLink>. Apr2026, Vol. 25 Issue 2, p192-208. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Data+visualization%22">Data visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Visualization%22">Visualization</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+representation%22">Mental representation</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Illustrating data visually through graphs enables the examination of valuable insights and the identification of crucial patterns that are beneficial for the user. However, as the volume of data grows, it becomes increasingly challenging to organize corresponding graphs and zero in on specific objects and/or relations of interest. Various techniques have been developed to tackle the complexity of large graphs, yet these methods operate independently, potentially resulting in inconsistencies and incorrect behaviors, as well as inefficient use of computing resources when mixed together and applied in a certain order. Furthermore, administering these methods can lead to significant changes in the layout of the graph, potentially disorienting the user and disrupting their mental map. This study aims to design a framework and associated algorithms for efficiently and effectively managing complexity during visual analysis of large relational data represented as graphs, by seamlessly integrating various complexity management techniques and making adjustments to the graph layout after each operation to preserve the user's mental map. A rendering-independent implementation of this framework and associated algorithms, as well as its integration with a popular graph rendering library named Cytoscape.js, can be accessed freely on GitHub. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edo&AN=192308870
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        Value: 10.1177/14738716251383173
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        Text: English
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      – SubjectFull: Visualization
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      – SubjectFull: Computational complexity
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      – SubjectFull: Mental representation
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      – SubjectFull: Graph theory
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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