Detecting subgraph isomorphism with MapReduce

In recent years, the MapReduce framework has become one of the most popular parallel computing platforms for processing big data. MapReduce is used by companies such as Facebook, IBM, and Google to process or analyze massive data sets. Since the approach is frequently used for industrial solutions, the algorithms based on the MapReduce framework gained significant attention within the scientific community. The subgraph isomorphism is a fundamental graph theory problem. Finding small patterns in large graphs is a core challenge in the analysis of applications with big data sets. This paper introduces two novel algorithms, which are capable of finding matching patterns in arbitrary large graphs. The algorithms are designed for utilizing the easy parallelization technique offered by the MapReduce framework. The approaches are evaluated regarding their space and memory requirements. The paper also provides the applied data structure and presents formal analysis of the algorithms.