A Survey on Distributed Graph Pattern Matching in Massive Graphs
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...
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| Veröffentlicht in: | ACM computing surveys 2021-04, Vol.54 (2), p.1-35 |
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| creator | Bouhenni, Sarra Yahiaoui, Saïd Nouali-Taboudjemat, Nadia Kheddouci, Hamamache |
| description | 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 article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models. |
| doi_str_mv | 10.1145/3439724 |
| format | Article |
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| ispartof | ACM computing surveys, 2021-04, Vol.54 (2), p.1-35 |
| issn | 0360-0300 1557-7341 |
| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_03141901v1 |
| source | ACM Digital Library Complete |
| subjects | Big Data Computer Science Distributed, Parallel, and Cluster Computing Graph matching Graph theory Graphs Isomorphism Pattern matching Polynomials Social networks |
| title | A Survey on Distributed Graph Pattern Matching in Massive Graphs |
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