GRAMI: Frequent subgraph and pattern mining in a single large graph

Handle URI:
http://hdl.handle.net/10754/334536
Title:
GRAMI: Frequent subgraph and pattern mining in a single large graph
Authors:
Elseidy, M.; Abdelhamid, Ehab ( 0000-0002-8708-0642 ) ; Skiadopoulos, S.; Kalnis, Panos ( 0000-0002-5060-1360 )
Abstract:
Mining 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.
KAUST Department:
King Abdullah University of Science and Technology, Saudi Arabia
Publisher:
VLDB Endowment
Journal:
Proceedings of the VLDB Endowment
Issue Date:
2014
DOI:
10.14778/2732286.2732289
Type:
Article
ISSN:
2150-8097
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorElseidy, M.en
dc.contributor.authorAbdelhamid, Ehaben
dc.contributor.authorSkiadopoulos, S.en
dc.contributor.authorKalnis, Panosen
dc.date.accessioned2014-11-11T14:28:51Zen
dc.date.available2014-11-11T14:28:51Zen
dc.date.issued2014en
dc.identifier.issn2150-8097en
dc.identifier.doi10.14778/2732286.2732289en
dc.identifier.urihttp://hdl.handle.net/10754/334536en
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.en
dc.language.isoenen
dc.publisherVLDB Endowmenten
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectFrequency thresholden
dc.subjectFrequent subgraph miningen
dc.subjectFrequent subgraphsen
dc.subjectImprove performanceen
dc.subjectModern applicationsen
dc.subjectOrders of magnitudeen
dc.subjectProtein-protein interactionsen
dc.subjectSemantic constraintsen
dc.subjectSemanticsen
dc.subjectBioinformaticsen
dc.titleGRAMI: Frequent subgraph and pattern mining in a single large graphen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technology, Saudi Arabiaen
dc.identifier.journalProceedings of the VLDB Endowmenten
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionEcole Polytechnique F?ed?erale de Lausanne, Switzerlanden
dc.contributor.institutionUniversity of Peloponnese, Greeceen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorKalnis, Panosen
kaust.authorAbdelhamid, Ehaben
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