2020/04/02 by Jongik Kim, Kim, Jongik
Computer Science · #Advanced Graph Neural Networks #Data Management and Algorithms #Databases (cs.DB) #FOS: Computer and information sciences #Graph Theory and Algorithms #cs.DB
paper · pdf · doi:10.48550/arxiv.2004.01124
arxiv created 2020/04/02 · openalex publication_date 2020/04/02 · arxiv updated 2020/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we study the problem of graph similarity search with graph edit distance (GED) constraints. Due to the NP-hardness of GED computation, existing solutions to this problem adopt the filtering-and-verification framework with a main focus on the filtering phase to generate a small number of candidate graphs. However, they have a limitation that the number of candidates grows extremely rapidly as a GED threshold increases. To address the limitation, we propose a new approach that utilizes GED computation results in generating candidate graphs. The main idea is that whenever we identify a result graph of the query, we immediately regenerate candidate graphs using a subset of pre-computed graphs similar to the identified result graph. To speed up GED computation, we also develop a novel GED computation algorithm. The proposed algorithm reduces the search space for GED computation by utilizing a series of filtering techniques, which have been used to generate candidates in existing solutions. Experimental results on real datasets demonstrate the proposed approach significantly outperforms the state-of-the art techniques.