Bouhenni, SarraYahiaoui, SaïdNouali-Taboudjemat, NadiaKheddouci, Hamamache2023-02-262023-02-262021-020360-03001557-7341https://dl.cerist.dz/handle/CERIST/965Besides 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.Theory of computationDistributed algorithmsGraph algorithms analysisComputing methodologiesGraph pattern matchingArticle