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dc.contributor.authorAbdelhamid, Ehab
dc.contributor.authorCanim, Mustafa
dc.contributor.authorSadoghi, Mohammad
dc.contributor.authorBhatta, Bishwaranjan
dc.contributor.authorChang, Yuan-Chi
dc.contributor.authorKalnis, Panos
dc.date.accessioned2017-10-09T09:03:13Z
dc.date.available2017-10-09T09:03:13Z
dc.date.issued2017-08-22
dc.identifier.citationAbdelhamid E, Canim M, Sadoghi M, Bhatta B, Chang Y-C, et al. (2017) Incremental Frequent Subgraph Mining on Large Evolving Graphs. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2017.2743075.
dc.identifier.issn1041-4347
dc.identifier.doi10.1109/TKDE.2017.2743075
dc.identifier.urihttp://hdl.handle.net/10754/625837
dc.description.abstractFrequent subgraph mining is a core graph operation used in many domains, such as graph data management and knowledge exploration, bioinformatics and security. Most existing techniques target static graphs. However, modern applications, such as social networks, utilize large evolving graphs. Mining these graphs using existing techniques is infeasible, due to the high computational cost. In this paper, we propose IncGM+, a fast incremental approach for continuous frequent subgraph mining problem on a single large evolving graph. 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 the efficiency, we propose an efficient index structure to maintain selected embeddings with minimal memory overhead. These embeddings are utilized to avoid redundant expensive subgraph isomorphism operations. Moreover, the proposed system supports batch updates. Using large real-world graphs, we experimentally verify that IncGM+ outperforms existing methods by up to three orders of magnitude, scales to much larger graphs and consumes less memory.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/8014497/
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectData mining
dc.subjectData mining
dc.subjectGraph algorithms
dc.subjectIndexes
dc.subjectIndexing
dc.subjectItemsets
dc.subjectMeasurement
dc.subjectMemory management
dc.subjectSecurity
dc.titleIncremental Frequent Subgraph Mining on Large Evolving Graphs
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Knowledge and Data Engineering
dc.eprint.versionPost-print
dc.contributor.institutionCognitive computing, IBM Thomas J Watson Research Center, 71353 Yorktown Heights, New York United States
dc.contributor.institutionComputer Science Department, Purdue University System, 8522 West Lafayette, Indiana United States
dc.contributor.institutionCognitive computing, IBM, Hawthorne, New York United States
dc.contributor.institutionCognitive computing, IBM, Yorktown Heights, New York United States
kaust.personAbdelhamid, Ehab
kaust.personKalnis, Panos
refterms.dateFOA2018-06-13T17:24:35Z
dc.date.published-online2017-08-22
dc.date.published-print2017-12-01


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