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.

中文翻译：

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海量图中的分布式图模式匹配研究

除了 NP 完全性之外，子图同构的严格约束使其在大数据环境中的图模式匹配 (GPM) 变得不切实际。结果，出现了宽松的 GPM 模型，因为它们在多项式时间内产生了有趣的结果。然而，大多数由社交网络生成的海量图需要在多台机器上分布式存储和处理数据，因此，需要通过采用新的大图处理范式来修改 GPM，例如 Think-Like-A-Vertex 及其衍生品. 本文讨论并提出了分布式 GPM 方法的分类，重点关注宽松模型。