When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities

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dc.contributor.authorPaulovich, Fernando V.en_US
dc.contributor.authorArleo, Alessioen_US
dc.contributor.authorElzen, Stef van denen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorAndrienko, Nataliaen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2025-05-26T06:36:29Z
dc.date.available2025-05-26T06:36:29Z
dc.date.issued2025
dc.description.abstractIn the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. DR aims to create representations to support neighborhood and similarity analysis on complex, large datasets. Graph analysis focuses on identifying the salient topological properties and key actors within network data, with specialized research investigating how such features could be presented to users to ease the comprehension of the underlying structure. Although these two disciplines are typically regarded as disjoint subfields, we argue that both fields share strong similarities and synergies that can potentially benefit both. Therefore, this paper discusses and introduces a unifying framework to help bridge the gap between DR and graph (drawing) theory. Our goal is to use the strongly math-grounded graph theory to improve the overall process of creating DR visual representations. We propose how to break the DR process into well-defined stages, discuss how to match some of the DR state-of-the-art techniques to this framework, and present ideas on how graph drawing, topology features, and some popular algorithms and strategies used in graph analysis can be employed to improve DR topology extraction, embedding generation, and result validation. We also discuss the challenges and identify opportunities for implementing and using our framework, opening directions for future visualization research.en_US
dc.description.sectionheadersDimensionality Reduction and High-Dimensional Data
dc.description.seriesinformationComputer Graphics Forum
dc.identifier.doi10.1111/cgf.70105
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70105
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70105
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visualization; Computing methodologies → Dimensionality reduction and manifold learning; Mathematics of computing → Graph theory
dc.subjectHuman centered computing → Visualization
dc.subjectComputing methodologies → Dimensionality reduction and manifold learning
dc.subjectMathematics of computing → Graph theory
dc.titleWhen Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunitiesen_US
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