Quick Mining of Isomorphic Exact Large Patterns from Large Graphs

Handle URI:
http://hdl.handle.net/10754/565845
Title:
Quick Mining of Isomorphic Exact Large Patterns from Large Graphs
Authors:
Almasri, Islam; Gao, Xin ( 0000-0002-7108-3574 ) ; Fedoroff, Nina V.
Abstract:
The applications of the sub graph isomorphism search are growing with the growing number of areas that model their systems using graphs or networks. Specifically, many biological systems, such as protein interaction networks, molecular structures and protein contact maps, are modeled as graphs. The sub graph isomorphism search is concerned with finding all sub graphs that are isomorphic to a relevant query graph, the existence of such sub graphs can reflect on the characteristics of the modeled system. The most computationally expensive step in the search for isomorphic sub graphs is the backtracking algorithm that traverses the nodes of the target graph. In this paper, we propose a pruning approach that is inspired by the minimum remaining value heuristic that achieves greater scalability over large query and target graphs. Our testing on various biological networks shows that performance enhancement of our approach over existing state-of-the-art approaches varies between 6x and 53x. © 2014 IEEE.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC); Structural and Functional Bioinformatics Group
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2014 IEEE International Conference on Data Mining Workshop
Conference/Event name:
14th IEEE International Conference on Data Mining Workshops, ICDMW 2014
Issue Date:
Dec-2014
DOI:
10.1109/ICDMW.2014.65
Type:
Conference Paper
Appears in Collections:
Conference Papers; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Biological and Environmental Sciences and Engineering (BESE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlmasri, Islamen
dc.contributor.authorGao, Xinen
dc.contributor.authorFedoroff, Nina V.en
dc.date.accessioned2015-08-11T13:43:05Zen
dc.date.available2015-08-11T13:43:05Zen
dc.date.issued2014-12en
dc.identifier.doi10.1109/ICDMW.2014.65en
dc.identifier.urihttp://hdl.handle.net/10754/565845en
dc.description.abstractThe applications of the sub graph isomorphism search are growing with the growing number of areas that model their systems using graphs or networks. Specifically, many biological systems, such as protein interaction networks, molecular structures and protein contact maps, are modeled as graphs. The sub graph isomorphism search is concerned with finding all sub graphs that are isomorphic to a relevant query graph, the existence of such sub graphs can reflect on the characteristics of the modeled system. The most computationally expensive step in the search for isomorphic sub graphs is the backtracking algorithm that traverses the nodes of the target graph. In this paper, we propose a pruning approach that is inspired by the minimum remaining value heuristic that achieves greater scalability over large query and target graphs. Our testing on various biological networks shows that performance enhancement of our approach over existing state-of-the-art approaches varies between 6x and 53x. © 2014 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectGraphsen
dc.subjectIsomorphismen
dc.subjectMining Networksen
dc.subjectSubgraphsen
dc.titleQuick Mining of Isomorphic Exact Large Patterns from Large Graphsen
dc.typeConference Paperen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentStructural and Functional Bioinformatics Groupen
dc.identifier.journal2014 IEEE International Conference on Data Mining Workshopen
dc.conference.date14 December 2014en
dc.conference.name14th IEEE International Conference on Data Mining Workshops, ICDMW 2014en
kaust.authorGao, Xinen
kaust.authorAlmasri, Islamen
kaust.authorFedoroff, Nina V.en
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