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

In 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 analy...

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Vydané v:Computer graphics forum Ročník 44; číslo 3
Hlavní autori: Paulovich, F. V., Arleo, A., van den Elzen, S.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.06.2025
Predmet:
CCS Concepts
Computing methodologies → Dimensionality reduction and manifold learning
Graph theory
Human‐centered computing → Visualization
Mathematics of computing → Graph theory
Representations
Topology
Visualization
ISSN:0167-7055, 1467-8659
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Popis
Shrnutí:In 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.
Bibliografia:ObjectType-Article-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.70105