Hong, S.-H., Liotta, G., Montecchiani, F., Nöllenburg, M., & Piselli, T. (2024). Introducing Fairness in Graph Visualization via Gradient Descent. In D. Archambault, I. T. Nabney, & J. Peltonen (Eds.), MLVis: Machine Learning Methods in Visualisation for Big Data (2024). Eurographics - The European Association for Computer Graphics. https://doi.org/10.2312/mlvis.20241124
MLVis 2024 colocated with EuroVis 2024 - 26th EG Conference on Visualization
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Event date:
27-May-2024
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Event place:
Odense, Denmark
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Number of Pages:
5
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Publisher:
Eurographics - The European Association for Computer Graphics
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Peer reviewed:
Yes
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Keywords:
CCS Concepts: Human-centered computing; Design and analysis of algorithms; Visualization; Theory of computation
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Abstract:
Motivated 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.