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GdMetriX - A NetworkX Extension For Graph Drawing Metrics (Poster Abstract)

Authors Martin Nöllenburg , Sebastian Röder , Markus Wallinger

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ACM Subject Classification
  • Human-centered computing → Graph drawings
Keywords
  • Graph Drawing Metrics

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Abstract

networkX is a well-established Python library for network analysis. With gdMetriX, we aim to extend the functionality of networkX and provide common quality metrics used in the field of graph drawing, such as the number of crossings or the angular resolution. In addition, the package provides easy-to-use access to the graph datasets provided by the ’Graph Layout Benchmark Datasets’ project from the Northeastern University Visualization Lab.

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Martin Nöllenburg, Sebastian Röder, and Markus Wallinger. GdMetriX - A NetworkX Extension For Graph Drawing Metrics (Poster Abstract). In 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 320, pp. 45:1-45:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.GD.2024.45

Author Details

Martin Nöllenburg
  • Algorithms and Complexity Group, TU Wien,Austria
Sebastian Röder
  • TU Wien, Austria
Markus Wallinger
  • Chair for Efficient Algorithms, Technical University of Munich, Germany

Funding

  • Nöllenburg, Martin: Vienna Science and Technology Fund (WWTF) [10.47379/ICT19035].
  • Wallinger, Markus: Vienna Science and Technology Fund (WWTF) [10.47379/ICT19035].

Supplementary Materials

References

  1. networkX homepage. https://networkx.org/. Accessed: 2024-07-20.
  2. Chris Bennett, Jody Ryall, Leo Spalteholz, and Amy Gooch. The Aesthetics of Graph Visualization. Computational Aesthetics in Graphics, 2007. URL: https://doi.org/10.2312/COMPAESTH/COMPAESTH07/057-064.
  3. Sara Di Bartolomeo, Eduardo Puerta, Connor Wilson, Tarik Crnovrsanin, and Cody Dunne. A Collection of Benchmark Datasets for Evaluating Graph Layout Algorithms. In Graph Drawing and Network Visualization (GD'23), volume 14466 of LNCS, pages 251-252. Springer, 2023. URL: https://doi.org/10.31219/osf.io/yftju.
  4. Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart. Exploring Network Structure, Dynamics, and Function using NetworkX. In Proceedings of the 7th Python in Science Conference, pages 11-15, 2008. Google Scholar
  5. Gavin J. Mooney, Helen C. Purchase, Michael Wybrow, and Stephen G. Kobourov. The Multi-Dimensional Landscape of Graph Drawing Metrics. In 2024 IEEE 17th Pacific Visualization Conference (PacificVis), pages 122-131. IEEE, 2024. URL: https://doi.org/10.1109/PacificVis60374.2024.00022.
  6. Helen C. Purchase. Metrics for Graph Drawing Aesthetics. Journal of Visual Languages & Computing, 13(5):501-516, 2002. URL: https://doi.org/10.1006/jvlc.2002.0232.
  7. Martyn Taylor and Peter Rodgers. Applying Graphical Design Techniques to Graph Visualisation. In Ninth International Conference on Information Visualisation (IV'05), pages 651-656, London, England, 2005. IEEE. URL: https://doi.org/10.1109/IV.2005.19.
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