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dc.contributor.authorAbdelhamid, Ehab
dc.contributor.authorCanim, Mustafa
dc.contributor.authorSadoghi, Mohammad
dc.contributor.authorBhattacharjee, Bishwaranjan
dc.contributor.authorChang, Yuan-Chi
dc.contributor.authorKalnis, Panos
dc.date.accessioned2018-12-30T08:41:31Z
dc.date.available2018-12-30T08:41:31Z
dc.date.issued2018-10-25
dc.identifier.citationAbdelhamid E, Canim M, Sadoghi M, Bhattacharjee B, Chang Y-C, et al. (2018) Incremental Frequent Subgraph Mining on Large Evolving Graphs. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00241.
dc.identifier.doi10.1109/ICDE.2018.00241
dc.identifier.urihttp://hdl.handle.net/10754/630372
dc.description.abstractFrequent subgraph mining is a core graph operation used in many domains. Most existing techniques target static graphs. However, modern applications utilize large evolving graphs. Mining these graphs using existing techniques is infeasible because of the high computational cost. We propose IncGM+, a fast incremental approach for frequent subgraph mining on large evolving graphs. We adapt the notion of 'fringe' to the graph context, that is, the set of subgraphs on the border between frequent and infrequent subgraphs. IncGM+ maintains fringe subgraphs and exploits them to prune the search space. To boost efficiency, IncGM+ stores a number of selected embeddings to avoid redundant expensive subgraph isomorphism operations. Moreover, the proposed system supports batch updates. Our results confirm that IncGM+ outperforms existing methods, scales to larger graphs and consumes less memory.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8509462
dc.rightsArchived with thanks to 2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.subjectEvolving graph
dc.subjectFrequent Subgraph Mining
dc.subjectIncremental Index
dc.titleIncremental Frequent Subgraph Mining on Large Evolving Graphs
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.contributor.departmentKing Abdullah University of Science and Technology, , United States
dc.identifier.journal2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.conference.date2018-04-16 to 2018-04-19
dc.conference.name34th IEEE International Conference on Data Engineering, ICDE 2018
dc.conference.locationParis, FRA
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionImperial College London, , United Kingdom
dc.contributor.institutionIBM Thomas J. Watson Research Center, , United States
dc.contributor.institutionUniversity of California, Davis, , United States
kaust.personKalnis, Panos
refterms.dateFOA2018-12-30T08:44:51Z
dc.date.published-online2018-10-25
dc.date.published-print2018-04


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