Diversified top-k search with relaxed graph simulation

Graph pattern matching has been widely used in a broad spectrum of real-world applications, and it has been the subject of several investigations, mainly of its importance and use. In this context, different models along with their appropriate algorithms have been proposed. However, in addition to the excessive processing costs, most of the existing models suffer from the failing query problem due to their limitations on finding meaningful matches. Also, in some scenarios, the number of matches may be enormous, making the inspection a daunting task. In this work, we introduce a new model for graph pattern matching, called relaxed graph simulation (RGS), allowing the relaxation of queries to identify more significant matches and to avoid the empty-set answer problem. We then formalize and study the top-k matching problem based on two function classes, relevance and diversity, for ranking the matches with respect to the proposed model. We also formalize and investigate the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Nonetheless, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the RGS model. Our experimental results, on four real datasets, demonstrate both the effectiveness and the efficiency of the proposed approaches.