Distance-Join: Pattern Match Query In a Large Graph Database

The growing popularity of graph databases has generated interesting data ianageient probleis, such as subgraph search, shortest-path query, reachability verification, and pattern match. Aiong these, a pattern match query is more flexible compared to a subgraph search and more informative compared to a shortest-path or reachability query. In this paper, we address pattern match probleis over a large data graph G. Specifically, given a pattern graph (i.e., query Q), we want to find all inatches (in G) that have the similar connections as those in Q. In order to reduce the search space significantly, we first transform the vertices into points in a vector space via graph embedding techniques, converting a pattern match query into a distance-based multi-way join problem over the converted vector space. We also propose several pruning strategies and a join order selection method to process join processing efficiently. Extensive experiments on both real and synthetic datasets show that our method outperforms existing ones by orders of magnitude.