A Survey on Distributed Graph Pattern Matching in Massive Graphs

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dc.contributor.authorBouhenni, Sarra
dc.contributor.authorYahiaoui, Saïd
dc.contributor.authorNouali-Taboudjemat, Nadia
dc.contributor.authorKheddouci, Hamamache
dc.date.accessioned2023-02-26T08:37:47Z
dc.date.available2023-02-26T08:37:47Z
dc.date.issued2021-02
dc.description.abstractBesides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.
dc.identifier.issn0360-0300
dc.identifier.issn1557-7341
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/965
dc.publisherACM
dc.relation.ispartofseriesACM Computing Surveys,; Vol. 54 - N° 2
dc.relation.pages1-35
dc.structureCalcul pervasif et mobile (Pervasive and Mobile Computing group)
dc.subjectTheory of computation
dc.subjectDistributed algorithms
dc.subjectGraph algorithms analysis
dc.subjectComputing methodologies
dc.subjectGraph pattern matching
dc.titleA Survey on Distributed Graph Pattern Matching in Massive Graphs
dc.typeArticle
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