Matching of matching-graphs - a novel approach for graph classification

dc.contributor.authorFuchs, Mathias
dc.contributor.authorRiesen, Kaspar
dc.date.accessioned2024-04-08T06:31:45Z
dc.date.available2024-04-08T06:31:45Z
dc.date.issued2020
dc.description.abstractDue to fast developments in data acquisition, we observe rapidly increasing amounts of data available in diverse areas. Simultaneously, we observe that in many applications the underlying data is inherently complex, making graphs a very useful and adequate data structure for formal representation. A large amount of graph based methods for pattern recognition have been proposed. Many of these methods actually rely on graph matching. In the present paper a novel encoding of graph matching information is proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs. In an empirical evaluation we show that it can be highly beneficial to focus on these stable parts of graphs during graph classification.
dc.eventInternational Conference on Pattern Recognition (ICPR) 2020
dc.event.end2021-01-15
dc.event.start2021-01-10
dc.identifier.doi10.1109/ICPR48806.2021.9411926
dc.identifier.isbn978-1-7281-8808-9
dc.identifier.isbn978-1-7281-8809-6
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/42881
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2020 25th International Conference on Pattern Recognition (ICPR)
dc.spatialMilano
dc.subject.ddc330 - Wirtschaft
dc.titleMatching of matching-graphs - a novel approach for graph classification
dc.type04B - Beitrag Konferenzschrift
dspace.entity.typePublication
fhnw.InventedHereYes
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Wirtschaftde_CH
fhnw.affiliation.institutInstitut für Wirtschaftsinformatikde_CH
fhnw.openAccessCategoryClosed
fhnw.pagination6570-6576
fhnw.publicationStatePublished
relation.isAuthorOfPublicationd761e073-1612-4d22-8521-65c01c19f97a
relation.isAuthorOfPublication.latestForDiscoveryd761e073-1612-4d22-8521-65c01c19f97a
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