Efficient processing of graph similarity search

Abstract

A graph similarity search is to find a set of graphs from a graph database that are similar to a given query graph. Existing works solve this problem by first defining a similarity measure between two graphs, and then presenting a filtering mechanism that reduces the number of candidate graphs. The candidate graphs are then verified by performing expensive graph search operations such as finding maximum common subgraphs. Existing works, however, do not report some similar graphs from a graph database while dissimilar graphs are not discarded during the filtering phase. To overcome this problem, in this paper, we first present a graph distance measure that can identify hidden but similar graphs that could not be discovered by previous graph distance measures. We then devise a series of filtering and validation rules to discard and identify non-matching and definitely-matching graphs, respectively, by calculating lower and upper bounds of the distance between a query and a data graph. To execute these filtering and validation rules efficiently during runtime, an index structure is also proposed. Lastly, a verification algorithm that verifies candidate graphs according to our graph distance measure is presented. Experiments on real datasets show that our approach can efficiently and effectively perform graph similarity search by significantly reducing the number of candidate graphs that must be verified, and by returning similar graphs.