Fast Top-k Search with Relaxed Graph Simulation

Graph pattern matching has been widely used in large spectrum of real applications. In this context, different models along with their appropriate algorithms have been proposed. However, a major drawback on existing models is their limitation to find meaningful matches resulting in a number of failing queries. In this paper we introduce a new model for graph pattern matching allowing the relaxation of queries in order to avoid the empty-answer problem. Then we develop an efficient algorithm based on optimization strategies for computing the top $k$ matches according to our model. Our experimental evaluation on four real datasets demonstrates both the effectiveness and the efficiency of our approach.