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.