Magnostics : Image-Based Search of Interesting Matrix Views for Guided Network Exploration
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
Internationale Patentnummer
EU-Projektnummer
DFG-Projektnummer
Projekt
Open Access-Veröffentlichung
Sammlungen
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
BEHRISCH, Michael, Benjamin BACH, Michael BLUMENSCHEIN, Michael DELZ, Laura VON RÜDEN, Jean-Daniel FEKETE, Tobias SCHRECK, 2017. Magnostics : Image-Based Search of Interesting Matrix Views for Guided Network Exploration. In: IEEE Transactions on Visualization and Computer Graphics. 2017, 23(1), pp. 31-40. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2016.2598467BibTex
@article{Behrisch2017-01Magno-38799, year={2017}, doi={10.1109/TVCG.2016.2598467}, title={Magnostics : Image-Based Search of Interesting Matrix Views for Guided Network Exploration}, number={1}, volume={23}, issn={1077-2626}, journal={IEEE Transactions on Visualization and Computer Graphics}, pages={31--40}, author={Behrisch, Michael and Bach, Benjamin and Blumenschein, Michael and Delz, Michael and von Rüden, Laura and Fekete, Jean-Daniel and Schreck, Tobias} }
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/123456789/38799"> <dc:contributor>Bach, Benjamin</dc:contributor> <dcterms:issued>2017-01</dcterms:issued> <dc:creator>Bach, Benjamin</dc:creator> <dc:contributor>Behrisch, Michael</dc:contributor> <dcterms:abstract xml:lang="eng">In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.</dcterms:abstract> <dc:creator>Schreck, Tobias</dc:creator> <dc:creator>Delz, Michael</dc:creator> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:creator>Fekete, Jean-Daniel</dc:creator> <dc:creator>Behrisch, Michael</dc:creator> <dcterms:title>Magnostics : Image-Based Search of Interesting Matrix Views for Guided Network Exploration</dcterms:title> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-05-09T08:57:49Z</dc:date> <dc:contributor>Delz, Michael</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-05-09T08:57:49Z</dcterms:available> <dc:creator>Blumenschein, Michael</dc:creator> <dc:contributor>Schreck, Tobias</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:creator>von Rüden, Laura</dc:creator> <dc:language>eng</dc:language> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>von Rüden, Laura</dc:contributor> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/38799"/> <dc:contributor>Fekete, Jean-Daniel</dc:contributor> <dc:contributor>Blumenschein, Michael</dc:contributor> </rdf:Description> </rdf:RDF>