Publikation:

Multiscale Visual Analysis of Dynamic Networks

Lade...
Vorschaubild

Dateien

Cakmak_2-1gpramms5xexa6.pdf
Cakmak_2-1gpramms5xexa6.pdfGröße: 19.5 MBDownloads: 267

Datum

2022

Autor:innen

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

DOI (zitierfähiger Link)
ArXiv-ID

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Dissertation
Publikationsstatus
Published

Erschienen in

Zusammenfassung

Networks are a universal language for modeling the underlying structure of real-world systems, such as computer networks, social networks, or financial networks. Many of these modeled real-world systems are dynamic, meaning the relationships between the entities change over time. A central goal in dynamic (temporal) network analysis is to discover similar network structures and retrace structural changes over time. However, visually analyzing dynamic networks remains challenging due to large-scale data often evolving over long periods. This thesis presents studies for the multiscale visual analysis of dynamic networks. The presented studies introduce multiscale dynamic network visualizations for identifying, comparing, tracing, and interpreting similar network structures over time. The proposed visualizations combine automated analysis methods with interactive visualizations to reveal evolving network structures across multiple abstraction scales (multiscale analysis). The presented multiscale visualizations scale to large-scale dynamic networks and enable analysts to relate high-level overviews with low-level details to reveal structural changes and similar network structures over time. The presented studies are showcased by prototype implementations using real-world datasets and are validated with domain experts, quantitative evaluations, and use cases. Moreover, the thesis systematically discusses the benefits and limitations of the presented studies and outlines future research perspectives.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690CAKMAK, Eren, 2022. Multiscale Visual Analysis of Dynamic Networks [Dissertation]. Konstanz: University of Konstanz
BibTex
@phdthesis{Cakmak2022Multi-59742,
  year={2022},
  title={Multiscale Visual Analysis of Dynamic Networks},
  author={Cakmak, Eren},
  address={Konstanz},
  school={Universität Konstanz}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/59742">
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-17T08:43:28Z</dc:date>
    <dc:contributor>Cakmak, Eren</dc:contributor>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59742/3/Cakmak_2-1gpramms5xexa6.pdf"/>
    <dc:language>eng</dc:language>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Multiscale Visual Analysis of Dynamic Networks</dcterms:title>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/59742"/>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2023-01-17T08:43:28Z</dcterms:available>
    <dc:creator>Cakmak, Eren</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/59742/3/Cakmak_2-1gpramms5xexa6.pdf"/>
    <dcterms:issued>2022</dcterms:issued>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:abstract xml:lang="eng">Networks are a universal language for modeling the underlying structure of real-world systems, such as computer networks, social networks, or financial networks. Many of these modeled real-world systems are dynamic, meaning the relationships between the entities change over time. A central goal in dynamic (temporal) network analysis is to discover similar network structures and retrace structural changes over time. However, visually analyzing dynamic networks remains challenging due to large-scale data often evolving over long periods. This thesis presents studies for the multiscale visual analysis of dynamic networks. The presented studies introduce multiscale dynamic network visualizations for identifying, comparing, tracing, and interpreting similar network structures over time. The proposed visualizations combine automated analysis methods with interactive visualizations to reveal evolving network structures across multiple abstraction scales (multiscale analysis). The presented multiscale visualizations scale to large-scale dynamic networks and enable analysts to relate high-level overviews with low-level details to reveal structural changes and similar network structures over time. The presented studies are showcased by prototype implementations using real-world datasets and are validated with domain experts, quantitative evaluations, and use cases. Moreover, the thesis systematically discusses the benefits and limitations of the presented studies and outlines future research perspectives.</dcterms:abstract>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

December 16, 2022
Hochschulschriftenvermerk
Konstanz, Univ., Diss., 2022
Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen