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 context big data. As a result, relaxed GPM models have emerged as they yield interesting results polynomial time. However, massive graphs generated by mostly social networks require distributed storing and processing data over multiple machines, thus, requiring to be revised adopting new paradigms processing, e.g., Think-Like-A-Vertex derivatives. This article discusses proposes classification approaches with narrow focus on models.