Motif-Based Visual Analysis of Dynamic Networks

Lade...
Vorschaubild
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2022
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
2022 IEEE Visualization in Data Science (VDS). Piscataway, NJ: IEEE, 2022, pp. 17-26. ISBN 978-1-6654-5721-7. Available under: doi: 10.1109/VDS57266.2022.00007
Zusammenfassung

Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences of selected sub-networks (motifs) and provide a time-scalable overview of dynamic networks: a network-level census (motif significance profiles) linked with a node-level sub-network metric (graphlet degree vectors) views to reveal structural changes, trends, states, and outliers. The network census captures significantly occurring motifs compared to their expected occurrences in random networks and exposes structural changes in a dynamic network. The sub-network metrics display the local topological neighborhood of a node in a single network belonging to the dynamic network. The linked pixel-based visualizations allow exploring motifs in different-sized networks to analyze the changing structures within and across dynamic networks, for instance, to visually analyze the shape and rate of changes in the network topology. We describe the identification of visual patterns, also considering different reordering strategies to emphasize visual patterns. We demonstrate the approach's usefulness by a use case analysis based on real-world large-scale dynamic networks, such as the evolving social networks of Reddit or Facebook.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
2022 IEEE Visualization in Data Science (VDS), 16. Okt. 2022 - 17. Okt. 2022
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690CAKMAK, Eren, Johannes FUCHS, Dominik JÄCKLE, Tobias SCHRECK, Ulrik BRANDES, Daniel A. KEIM, 2022. Motif-Based Visual Analysis of Dynamic Networks. 2022 IEEE Visualization in Data Science (VDS), 16. Okt. 2022 - 17. Okt. 2022. In: 2022 IEEE Visualization in Data Science (VDS). Piscataway, NJ: IEEE, 2022, pp. 17-26. ISBN 978-1-6654-5721-7. Available under: doi: 10.1109/VDS57266.2022.00007
BibTex
@inproceedings{Cakmak2022-08-25T08:27:36ZMotif-58421,
  year={2022},
  doi={10.1109/VDS57266.2022.00007},
  title={Motif-Based Visual Analysis of Dynamic Networks},
  isbn={978-1-6654-5721-7},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2022 IEEE Visualization in Data Science (VDS)},
  pages={17--26},
  author={Cakmak, Eren and Fuchs, Johannes and Jäckle, Dominik and Schreck, Tobias and Brandes, Ulrik and Keim, Daniel A.}
}
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/123456789/58421">
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:contributor>Brandes, Ulrik</dc:contributor>
    <dc:contributor>Cakmak, Eren</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-08-30T10:17:31Z</dc:date>
    <dcterms:title>Motif-Based Visual Analysis of Dynamic Networks</dcterms:title>
    <dc:creator>Schreck, Tobias</dc:creator>
    <dcterms:issued>2022-08-25T08:27:36Z</dcterms:issued>
    <dc:creator>Brandes, Ulrik</dc:creator>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/58421"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Jäckle, Dominik</dc:contributor>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/58421/1/Cakmak_2-1vaxso6t4hzja4.pdf"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-08-30T10:17:31Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dc:creator>Jäckle, Dominik</dc:creator>
    <dcterms:abstract xml:lang="eng">Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks remains challenging due to the underlying large-scale data containing elusive topological changes. We propose two complementary pixel-based visualizations, which reflect occurrences of selected sub-networks (motifs) and provide a time-scalable overview of dynamic networks: a network-level census (motif significance profiles) linked with a node-level sub-network metric (graphlet degree vectors) views to reveal structural changes, trends, states, and outliers. The network census captures significantly occurring motifs compared to their expected occurrences in random networks and exposes structural changes in a dynamic network. The sub-network metrics display the local topological neighborhood of a node in a single network belonging to the dynamic network. The linked pixel-based visualizations allow exploring motifs in different-sized networks to analyze the changing structures within and across dynamic networks, for instance, to visually analyze the shape and rate of changes in the network topology. We describe the identification of visual patterns, also considering different reordering strategies to emphasize visual patterns. We demonstrate the approach's usefulness by a use case analysis based on real-world large-scale dynamic networks, such as the evolving social networks of Reddit or Facebook.</dcterms:abstract>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Cakmak, Eren</dc:creator>
    <dc:contributor>Schreck, Tobias</dc:contributor>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:contributor>Fuchs, Johannes</dc:contributor>
    <dc:creator>Fuchs, Johannes</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/58421/1/Cakmak_2-1vaxso6t4hzja4.pdf"/>
  </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
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen