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dc.contributor.authorRiva, M.
HAL ID: 177894
ORCID: 0000-0003-0054-4131
dc.contributor.authorYger, Florian
HAL ID: 17768
ORCID: 0000-0002-7182-8062
dc.contributor.authorGori, P.
dc.contributor.authorCesar, R.
dc.contributor.authorBloch, I.
dc.date.accessioned2020-11-04T16:43:18Z
dc.date.available2020-11-04T16:43:18Z
dc.date.issued2020
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/21189
dc.language.isoenen
dc.subjectgraph clustering
dc.subjectgraph matching
dc.subjectgraph segmentation
dc.subjectstructural prior
dc.subject.ddc006.3en
dc.titleTemplate-Based Graph Clustering
dc.typeCommunication / Conférence
dc.description.abstractenWe propose a novel graph clustering method guided by additional information on the underlying structure of the clusters (or communities). The problem is formulated as the matching of a graph to a template with smaller dimension, hence matching n vertices of the observed graph (to be clustered) to the k vertices of a template graph, using its edges as support information, and relaxed on the set of orthonormal matrices in order to find a k dimensional embedding. With relevant priors that encode the density of the clusters and their relationships, our method outperforms classical methods, especially for challenging cases.
dc.subject.ddclabelIntelligence artificielleen
dc.relation.conftitleECML-PKDD, Workshop on Graph Embedding and Minin (GEM)
dc.relation.confdate2020-09
dc.relation.confcityGhent
dc.relation.confcountryBELGIUM
dc.relation.forthcomingnonen
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2021-01-12T14:41:55Z


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