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Measuring the quality of network visualization

Published: 07 June 2005 Publication History

Abstract

A quantitative method is developed for measuring the quality of network visualizations in terms of log-likelihood metrics resulted from Expectation Maximization (EM) clustering intrinsic and extrinsic attributes of network nodes.

References

[1]
Chen, C. Information Visualization: Beyond the Horizon. London: Springer Verlag.
[2]
Chen, C. Searching for intellectual turning points: Progressive Knowledge Domain Visualization. Proc. Natl. Acad. Sci. USA, 101 (2004), 5303--5310.
[3]
Chen, C. CiteSpace. http://cluster.cis.drexel.edu/~cchen

Cited By

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  • (2024)Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpaceIEEE Access10.1109/ACCESS.2023.334902812(3499-3513)Online publication date: 2024
  • (2015)Explicit frequency control for high-quality texture-based flow visualizationProceedings of the 2015 IEEE Scientific Visualization Conference (SciVis)10.1109/SciVis.2015.7429490(41-48)Online publication date: 25-Oct-2015
  • (2013)A Metric for the Evaluation of Dense Vector Field VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2012.17019:7(1122-1132)Online publication date: 1-Jul-2013
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Published In

cover image ACM Conferences
JCDL '05: Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
June 2005
450 pages
ISBN:1581138768
DOI:10.1145/1065385
  • General Chair:
  • Mary Marlino,
  • Program Chairs:
  • Tamara Sumner,
  • Frank Shipman
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2005

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Author Tags

  1. EM clustering
  2. network visualization
  3. quality metrics of quality

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JCDL05

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Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

View all
  • (2024)Analysis of the Research Progress of Electromagnetic Railgun Based on CiteSpaceIEEE Access10.1109/ACCESS.2023.334902812(3499-3513)Online publication date: 2024
  • (2015)Explicit frequency control for high-quality texture-based flow visualizationProceedings of the 2015 IEEE Scientific Visualization Conference (SciVis)10.1109/SciVis.2015.7429490(41-48)Online publication date: 25-Oct-2015
  • (2013)A Metric for the Evaluation of Dense Vector Field VisualizationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2012.17019:7(1122-1132)Online publication date: 1-Jul-2013
  • (2010)A salience-based quality metric for visualizationProceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization10.1111/j.1467-8659.2009.01667.x(1183-1192)Online publication date: 9-Jun-2010

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