D-colSimulation: A Distributed Approach for Frequent Graph Pattern Mining based on colSimulation in a Single Large Graph

Abstract

Frequent pattern mining in graph data is a hot topic in recent years. At present, most frequent graph pattern mining methods use the concept of subgraph isomorphism for the matching of candidate graph pattern in data graph. However, in some applications where the accuracy of matching is not so strict, the topology constraints of subgraph isomorphism may lose some meaningful frequent patterns. Simulation matching plays an important role in graph pattern matching. However, in frequent graph pattern mining, it may lead to the matching between the connected candidate pattern and the disconnected substructure in the data graph. The topology of matching results can not be guaranteed, which greatly affects the quality of mining, and may lead to mining a large number of redundant graphics patterns with repeated structure. Therefore, this paper proposes a new concept of simulation matching - colSimulation, which can ensure the point-to-point matching between pattern graph and data graph, effectively avoid redundant mining results and improve the mining speed. The D-colSimulation proposed in this paper is a distributed frequent graph pattern mining method based on colSimulation for large-scale graph data. Experiments on datasets show that our method not only improves the mining efficiency, but also performs well on data sets with poor performance of subgraph isomorphism.