Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models
Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models
Loading...
Date
2019
Authors
Editors
Journal ISSN
Electronic ISSN
ISBN
Bibliographical data
Publisher
Series
URI (citable link)
International patent number
Link to the license
EU project number
700381
Project
ASGARD - Analysis System For Gathered Raw Data
Open Access publication
Collections
Title in another language
Publication type
Contribution to a conference collection
Publication status
Published
Published in
Proceeedings of the Set Visual Analytics Workshop at IEEE VIS 2019
Abstract
Members of communities often share topics of interest. However, usually not all members are interested in all topics, and participation in topics changes over time. Prediction models based on temporal hypergraphs that—in contrast to state-of-the-art models—exploit group structures in the communication network can be used to anticipate changes of interests. In practice, there is a need to assess these models in detail. While loss functions used in the training process can provide initial cues on the model’s global quality, local quality can be investigated with visual analytics. In this paper, we present a visual analytics framework for the assessment of temporal hypergraph prediction models. We introduce its core components: a sliding window approach to prediction and an interactive visualization for partially fuzzy temporal hypergraphs.
Summary in another language
Subject (DDC)
004 Computer Science
Keywords
Conference
Set Visual Analytics Workshop at IEEE VIS 2019, Oct 20, 2019, Vancouver, Canada
Review
undefined / . - undefined, undefined. - (undefined; undefined)
Cite This
ISO 690
STREEB, Dirk, Devanshu ARYA, Daniel A. KEIM, Marcel WORRING, 2019. Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models. Set Visual Analytics Workshop at IEEE VIS 2019. Vancouver, Canada, Oct 20, 2019. In: Proceeedings of the Set Visual Analytics Workshop at IEEE VIS 2019BibTex
@inproceedings{Streeb2019Visua-47306, year={2019}, title={Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models}, url={https://scibib.dbvis.de/publications/view/838}, booktitle={Proceeedings of the Set Visual Analytics Workshop at IEEE VIS 2019}, author={Streeb, Dirk and Arya, Devanshu 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/123456789/47306"> <dc:creator>Streeb, Dirk</dc:creator> <dc:contributor>Streeb, Dirk</dc:contributor> <dc:contributor>Arya, Devanshu</dc:contributor> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/47306/1/Streeb_2-e6kfi4g07dsr7.pdf"/> <dc:language>eng</dc:language> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:creator>Keim, Daniel A.</dc:creator> <dc:creator>Worring, Marcel</dc:creator> <dcterms:abstract xml:lang="eng">Members of communities often share topics of interest. However, usually not all members are interested in all topics, and participation in topics changes over time. Prediction models based on temporal hypergraphs that—in contrast to state-of-the-art models—exploit group structures in the communication network can be used to anticipate changes of interests. In practice, there is a need to assess these models in detail. While loss functions used in the training process can provide initial cues on the model’s global quality, local quality can be investigated with visual analytics. In this paper, we present a visual analytics framework for the assessment of temporal hypergraph prediction models. We introduce its core components: a sliding window approach to prediction and an interactive visualization for partially fuzzy temporal hypergraphs.</dcterms:abstract> <foaf:homepage rdf:resource="http://localhost:8080/"/> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/47306"/> <dc:creator>Arya, Devanshu</dc:creator> <dcterms:title>Visual Analytics Framework for the Assessment of Temporal Hypergraph Prediction Models</dcterms:title> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/47306/1/Streeb_2-e6kfi4g07dsr7.pdf"/> <dcterms:issued>2019</dcterms:issued> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-10-24T12:50:31Z</dcterms:available> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2019-10-24T12:50:31Z</dc:date> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:rights>terms-of-use</dc:rights> <dc:contributor>Worring, Marcel</dc:contributor> </rdf:Description> </rdf:RDF>
Internal note
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
URL of original publication
Test date of URL
2019-10-24
Examination date of dissertation
Method of financing
Comment on publication
Alliance license
Corresponding Authors der Uni Konstanz vorhanden
International Co-Authors
Bibliography of Konstanz
Yes
Refereed
Version History
You are currently viewing version 1 of the item.