Visual Analytics for Temporal Hypergraph Model Exploration

Lade...
Vorschaubild
Dateien
Fischer_2-1mfa2sxnu9atk9.pdf
Fischer_2-1mfa2sxnu9atk9.pdfGröße: 3.24 MBDownloads: 200
Datum
2021
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
Internationale Patentnummer
Angaben zur Forschungsförderung
European Union (EU): 700381
Projekt
ASGARD - Analysis System For Gathered Raw Data
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
IEEE Transactions on Visualization and Computer Graphics. IEEE. 2021, 27(2), pp. 550-560. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2020.3030408
Zusammenfassung

Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Hypergraph, communication analysis, geometric deep learning, semantic zoom, matrix ordering, visual analytics
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690FISCHER, Maximilian T., Devanshu ARYA, Dirk STREEB, Daniel SEEBACHER, Daniel A. KEIM, Marcel WORRING, 2021. Visual Analytics for Temporal Hypergraph Model Exploration. In: IEEE Transactions on Visualization and Computer Graphics. IEEE. 2021, 27(2), pp. 550-560. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2020.3030408
BibTex
@article{Fischer2021-02Visua-53080,
  year={2021},
  doi={10.1109/TVCG.2020.3030408},
  title={Visual Analytics for Temporal Hypergraph Model Exploration},
  number={2},
  volume={27},
  issn={1077-2626},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  pages={550--560},
  author={Fischer, Maximilian T. and Arya, Devanshu and Streeb, Dirk and Seebacher, Daniel and Keim, Daniel A. and Worring, Marcel}
}
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/123456789/53080">
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Fischer, Maximilian T.</dc:contributor>
    <dc:contributor>Keim, Daniel A.</dc:contributor>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-05T09:39:24Z</dcterms:available>
    <dc:contributor>Seebacher, Daniel</dc:contributor>
    <dc:creator>Streeb, Dirk</dc:creator>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Keim, Daniel A.</dc:creator>
    <dc:creator>Fischer, Maximilian T.</dc:creator>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53080/1/Fischer_2-1mfa2sxnu9atk9.pdf"/>
    <dc:contributor>Arya, Devanshu</dc:contributor>
    <dcterms:abstract xml:lang="eng">Many processes, from gene interaction in biology to computer networks to social media, can be modeled more precisely as temporal hypergraphs than by regular graphs. This is because hypergraphs generalize graphs by extending edges to connect any number of vertices, allowing complex relationships to be described more accurately and predict their behavior over time. However, the interactive exploration and seamless refinement of such hypergraph-based prediction models still pose a major challenge. We contribute Hyper-Matrix, a novel visual analytics technique that addresses this challenge through a tight coupling between machine-learning and interactive visualizations. In particular, the technique incorporates a geometric deep learning model as a blueprint for problem-specific models while integrating visualizations for graph-based and category-based data with a novel combination of interactions for an effective user-driven exploration of hypergraph models. To eliminate demanding context switches and ensure scalability, our matrix-based visualization provides drill-down capabilities across multiple levels of semantic zoom, from an overview of model predictions down to the content. We facilitate a focused analysis of relevant connections and groups based on interactive user-steering for filtering and search tasks, a dynamically modifiable partition hierarchy, various matrix reordering techniques, and interactive model feedback. We evaluate our technique in a case study and through formative evaluation with law enforcement experts using real-world internet forum communication data. The results show that our approach surpasses existing solutions in terms of scalability and applicability, enables the incorporation of domain knowledge, and allows for fast search-space traversal. With the proposed technique, we pave the way for the visual analytics of temporal hypergraphs in a wide variety of domains.</dcterms:abstract>
    <dcterms:issued>2021-02</dcterms:issued>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-03-05T09:39:24Z</dc:date>
    <dc:contributor>Worring, Marcel</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Streeb, Dirk</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>Arya, Devanshu</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Visual Analytics for Temporal Hypergraph Model Exploration</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/53080/1/Fischer_2-1mfa2sxnu9atk9.pdf"/>
    <dc:language>eng</dc:language>
    <dc:creator>Seebacher, Daniel</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/53080"/>
    <dc:creator>Worring, Marcel</dc:creator>
  </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
Ja
Diese Publikation teilen