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dc.contributor.authorElseidy, M.
dc.contributor.authorAbdelhamid, Ehab
dc.contributor.authorSkiadopoulos, S.
dc.contributor.authorKalnis, Panos
dc.date.accessioned2014-11-11T14:28:51Z
dc.date.available2014-11-11T14:28:51Z
dc.date.issued2014
dc.identifier.issn2150-8097
dc.identifier.doi10.14778/2732286.2732289
dc.identifier.urihttp://hdl.handle.net/10754/334536
dc.description.abstractMining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or proteinprotein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GRAMI, a novel framework for frequent subgraph mining in a single large graph. GRAMI undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GRAMI that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGRAMI, a version supporting structural and semantic constraints, and AGRAMI, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches. 2014 VLDB Endowment.
dc.language.isoen
dc.publisherVLDB Endowment
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subjectFrequency threshold
dc.subjectFrequent subgraph mining
dc.subjectFrequent subgraphs
dc.subjectImprove performance
dc.subjectModern applications
dc.subjectOrders of magnitude
dc.subjectProtein-protein interactions
dc.subjectSemantic constraints
dc.subjectSemantics
dc.subjectBioinformatics
dc.titleGRAMI: Frequent subgraph and pattern mining in a single large graph
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalProceedings of the VLDB Endowment
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionEcole Polytechnique F?ed?erale de Lausanne, Switzerland
dc.contributor.institutionUniversity of Peloponnese, Greece
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personKalnis, Panos
kaust.personAbdelhamid, Ehab
refterms.dateFOA2018-06-14T03:46:01Z


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