Focusing on low accuracy and high costs of backtracking-based subgraph query algorithm applied to large-scale graphs, a Spark-based Subgraph Query Matching (SQM) algorithm was proposed to improve query accuracy and reduce query overhead for large graphs. The data graph was firstly filtered according to structure information, and the query graph was divided into basic query units. Then each basic query unit was matched and joined together. Finally, the algorithm's efficiency was improved and search space was reduced by parallelization. The experimental results show that compared with Stwig (Sub twig) algorithm and TurboISO algorithm, SQM algorithm can increase the speed by 50% while ensuring the same query results.

As a type of data structure representing entities, graphs are widely used in fields that have high requirements on data relevance, such as community data discovery, biochemical analysis and social security analysis. Focusing on the issue of real-time graph query operation under large-scale data, building a suitable index can effectively reduce query response time and improve query accuracy. The basic structure of index-based subgraph query algorithm was firstly introduced and then state-of-the-art algorithms were divided into two categories by construction method of index:enumeration construction and frequent pattern mining construction. Then these algorithms were introduced and analyzed from three aspects:index features, index structures and application datasets. Finally, main problems toward index-based subgraph query algorithm were summarized and analyzed, the latest query technology based on the distributed system was briefly described, and the future trend was forecasted.