Introducing Fairness in Graph Visualization (Poster Abstract)

Authors Seok-Hee Hong , Giuseppe Liotta , Fabrizio Montecchiani , Martin Nöllenburg , Tommaso Piselli



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Author Details

Seok-Hee Hong
  • University of Sydney, Australia
Giuseppe Liotta
  • University of Perugia, Italy
Fabrizio Montecchiani
  • University of Perugia, Italy
Martin Nöllenburg
  • TU Wien, Austria
Tommaso Piselli
  • University of Perugia, Italy

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Seok-Hee Hong, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nöllenburg, and Tommaso Piselli. Introducing Fairness in Graph Visualization (Poster Abstract). In 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 320, pp. 49:1-49:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.GD.2024.49

Abstract

Information visualization tools are an essential component of many data-driven decision-making systems that rely on human feedback. The aim of this paper is to propose a novel research direction focused on fair visualizations of graphs.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Graph drawings
Keywords
  • Network visualization
  • Fairness
  • Stress minimization

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References

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  4. Emden R. Gansner, Yehuda Koren, and Stephen C. North. Graph drawing by stress majorization. In János Pach, editor, GD 2004, volume 3383 of Lecture Notes in Computer Science, pages 239-250. Springer, 2004. URL: https://doi.org/10.1007/978-3-540-31843-9_25.
  5. Jane Hoffswell, Alan Borning, and Jeffrey Heer. Setcola: High-level constraints for graph layout. Comput. Graph. Forum, 37(3):537-548, 2018. URL: https://doi.org/10.1111/cgf.13440.
  6. Seok-Hee Hong, Giuseppe Liotta, Fabrizio Montecchiani, Martin Nöllenburg, and Tommaso Piselli. Introducing Fairness in Graph Visualization via Gradient Descent. In Daniel Archambault, Ian Nabney, and Jaakko Peltonen, editors, Machine Learning Methods in Visualisation for Big Data. The Eurographics Association, 2024. URL: https://doi.org/10.2312/mlvis.20241124.
  7. Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. A survey on bias and fairness in machine learning. ACM Comput. Surv., 54(6):115:1-115:35, 2022. URL: https://doi.org/10.1145/3457607.
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