An efficient and scalable approach for mining subgraphs in a single large graph

Abstract

In many recent applications, a graph is used to simulate many complex systems, such as social networks, traffic models or bioinformatics, and the underlying graphs for these systems are very large. Algorithms for mining all frequent subgraphs from a single large graph have attracted much attention and been studied in more detail lately. Mining frequent subgraphs is important, and defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. Among frequent subgraph algorithms, GraMi is considered as the state-of-the-art approach. However, GraMi has a huge search space, and therefore still needs a lot of time and memory to process a large graph. In this paper, we propose two effective strategies to balance and reduce the search space of GraMi, which can decrease the number of candidate subgraphs generated, with early pruning of a large portion of the domain for each candidate. Our experiments were performed on four real datasets and the results show that the performance of our balancing GraMi is better than those of the original algorithm GraMi and the optimized version SoGraMi.