Analysis of Network Ensembles

Lade...
Vorschaubild
Dateien
nagel_212891.pdf
nagel_212891.pdfGröße: 5.93 MBDownloads: 258
Datum
2011
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
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Dissertation
Publikationsstatus
Published
Erschienen in
Zusammenfassung

Subject of this dissertation is the assessment of graph similarity. The application context and ultimate aim is the analysis of network ensembles, i.e. collections of networks, in the sense of identifying structure among them, e.g. groups of highly similar networks. Structure is in this context understood as some form of regularity or description of the similarities among the considered networks.

As an illustration, consider a collection of two types of networks, where networks of the same type are very similar, while networks of different types are very dissimilar. These two groups form some kind of similarity that is of interest when the ensemble is the object to be analyzed.

Consequently, graphs are in this situation the elementary entities and the main interest is the measurement of structural similarities between them.


The interest in graphs as opposed to e.g. vectors as basic objects is motivated by their descriptive capabilities: some objects, e.g. electric circuits, social networks, comprehend important structural properties that can be expressed directly by modeling them as graphs. They have also found to be a powerful description mechanism for objects that do not incorporate an obvious relational structure as for example in image recognition.


Using graphs to describe objects leads to sets or collections of graphs on which problems of supervised and unsupervised learning are to be solved. A fundamental prerequisite in such approaches is the ability to compare the elementary objects, i.e. assess similarity or dissimilarity between them. For a number supervised and unsupervised learning algorithms a similarity or distance on the objects of analysis is even the sole prerequisite for their application, a prominent example given by support vector machines (c.f.Vapnik(1998)). Motivated by these considerations, three approaches for assessing and measuring similarity between graphs are developed.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
network ensembles, graph comparison, spectral distance, Ungerichteter Graph, Maschinelles Lernen, Diskretes Spektrum
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690NAGEL, Uwe, 2011. Analysis of Network Ensembles [Dissertation]. Konstanz: University of Konstanz
BibTex
@phdthesis{Nagel2011Analy-21289,
  year={2011},
  title={Analysis of Network Ensembles},
  author={Nagel, Uwe},
  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/123456789/21289">
    <dc:contributor>Nagel, Uwe</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-01-29T09:10:43Z</dc:date>
    <dc:rights>terms-of-use</dc:rights>
    <dcterms:abstract xml:lang="eng">Subject of this dissertation is the assessment of graph similarity. The application context and ultimate aim is the analysis of network ensembles, i.e. collections of networks, in the sense of identifying structure among them, e.g. groups of highly similar networks. Structure is in this context understood as some form of regularity or description of the similarities among the considered networks.&lt;br /&gt;&lt;br /&gt;As an illustration, consider a collection of two types of networks, where networks of the same type are very similar, while networks of different types are very dissimilar. These two groups form some kind of similarity that is of interest when the ensemble is the object to be analyzed.&lt;br /&gt;&lt;br /&gt;Consequently, graphs are in this situation the elementary entities and the main interest is the measurement of structural similarities between them.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;The interest in graphs as opposed to e.g. vectors as basic objects is motivated by their descriptive capabilities: some objects, e.g. electric circuits, social networks, comprehend important structural properties that can be expressed directly by modeling them as graphs. They have also found to be a powerful description mechanism for objects that do not incorporate an obvious relational structure as for example in image recognition.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;Using graphs to describe objects leads to sets or collections of graphs on which problems of supervised and unsupervised learning are to be solved. A fundamental prerequisite in such approaches is the ability to compare the elementary objects, i.e. assess similarity or dissimilarity between them. For a number supervised and unsupervised learning algorithms a similarity or distance on the objects of analysis is even the sole prerequisite for their application, a prominent example given by support vector machines (c.f.Vapnik(1998)). Motivated by these considerations, three approaches for assessing and measuring similarity between graphs are developed.</dcterms:abstract>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/21289/1/nagel_212891.pdf"/>
    <dcterms:title>Analysis of Network Ensembles</dcterms:title>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-01-29T09:10:43Z</dcterms:available>
    <dc:language>eng</dc:language>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:creator>Nagel, Uwe</dc:creator>
    <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/21289"/>
    <dcterms:issued>2011</dcterms:issued>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/21289/1/nagel_212891.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
December 7, 2011
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen