Graphs enjoy profound importance because of their versatility and expressivity. They can be effectively used to represent social networks, search engines and genome sequencing. The field of subgraph pattern matching has been of significant importance and has wide-spread applications. Conceptually, we want to find subgraphs that match a pattern in a given graph. Much work has been done in this field with solutions like Subgraph Isomorphism and Regular Expression matching. With Big Data, scientists are frequently running into massive graphs that have amplified the challenge that this area poses. We study the speedup and communication behavior of three distributed algorithms that we proposed for inexact pattern matching. We also study the impact of different graph partitionings on runtime and communication. Our extensive results show that the algorithms exhibit excellent scalable behavior and min-cut partitioning can lead to improved performance under some circumstances, and can also drastically reduce the network traffic.