  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.
