OWGraMi: Efficient method for mining weighted subgraphs in a single graph

Abstract

Recently, the problem of mining weighted subgraphs from a weighted single graph has become a vital issue because weighted graphs are generally used to restore, simulate or monitor many complex and large systems in which each object has a different role/level. This field has attracted the attention of numerous researchers, and within related studies Weighted Graph Mining (WeGraMi) can be considered as the state-of-the-art method. However, WeGraMi lacks a strategy to prune unweighted candidate subgraphs early in the process and needs a lot of time to compute the weight for all mined frequent subgraphs. In this paper, we optimize the WeGraMi algorithm with the use of two effective strategies in a new method, which we call Optimized Weighted Graph Mining (OWGraMi). Firstly, we use a strategy to prune all frequent edges which cannot reach the weighting threshold, and with this method we can decrease the number of unweighted candidates. Secondly, we reuse the weight of parent subgraphs when computing the weight for their child subgraphs, as this can reduce the running time for the mining process. On two real graph datasets, directed as well as undirected, our experiments show that both the running time and memory requirements for OWGraMi can be reduced significantly in comparison to the original algorithm.