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A Survey on Distributed Graph Pattern Matching in Massive Graphs

Abstract : Besides 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 paper discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.
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Submitted on : Friday, February 26, 2021 - 12:36:40 PM
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Sarra Bouhenni, Said Yahiaoui, Nadia Nouali-Taboudjemat, Hamamache Kheddouci. A Survey on Distributed Graph Pattern Matching in Massive Graphs. ACM Computing Surveys, Association for Computing Machinery, 2021, 54 (2), pp.36. ⟨10.1145/3439724⟩. ⟨hal-03141901⟩



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