Introducing Fairness in Graph Visualization via Gradient Descent

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dc.contributor.authorHong, Seok-Heeen_US
dc.contributor.authorLiotta, Giuseppeen_US
dc.contributor.authorMontecchiani, Fabrizioen_US
dc.contributor.authorNöllenburg, Martinen_US
dc.contributor.authorPiselli, Tommasoen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorNabney, Ianen_US
dc.contributor.editorPeltonen, Jaakkoen_US
dc.date.accessioned2024-05-21T08:51:12Z
dc.date.available2024-05-21T08:51:12Z
dc.date.issued2024
dc.description.abstractMotivated by the need for decision-making systems that avoid bias and discrimination, the concept of fairness recently gained traction in the broad field of artificial intelligence, stimulating new research also within the information visualization community. In this paper, we introduce a notion of fairness in network visualization, specifically for straight-line drawings of graphs, a foundational paradigm in the field. We empirically investigate the following research questions: (i) What is the price of incorporating fairness constraints in straight-line drawings? (ii) How unfair is a straight-line drawing that does not optimize fairness as a primary objective? To tackle these questions, we implement an algorithm based on gradient-descent that can compute straight-line drawings of graphs by optimizing multi-objective functions. We experimentally show that one can significantly increase the fairness of a drawing by paying a relatively small amount in terms of reduced readability.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20241124
dc.identifier.isbn978-3-03868-256-1
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/mlvis.20241124
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/mlvis20241124
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualization; Theory of computation → Design and analysis of algorithms
dc.subjectHuman centered computing → Visualization
dc.subjectTheory of computation → Design and analysis of algorithms
dc.titleIntroducing Fairness in Graph Visualization via Gradient Descenten_US
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Machine Learning Methods in Visualisation for Big Data 2024