Subgraph Matching Based on Path Adaptation for Large-Scale Graph

Xinmiao Hu, Sui Lin, Guangsi Xiong, Wenchao Jiang

Abstract: To increase the accuracy and decrease the cost of subgraph querying in large-scale graph data, a subgraph matching method based on path adaptation is proposed. An index is built based on path RDF, and the query graph is decomposed into a set of paths to obtain a set of candidate paths for each path. Then, the candidate paths are concatenated together through K-Partition intersection. Experimental results show that the query accuracy is improved by 15% on average compared with SPATH and SPARRER when dealing with large-scale data.