Efficient Top-k Graph Similarity Search With GED Constraints

It is essential to identify similarity between graphs for various tasks in data mining, machine learning and pattern recognition. Graph edit distance (GED) is the most popular graph similarity measure thanks to its flexibility and versatility. In this paper, we study the problem of top-
$k$
graph similarity search, which finds
$k$
graphs most similar to a given query graph under the GED measure. We propose incremental GED computation algorithms that compute desired GED lower and upper bounds. Based on the algorithms, we develop novel search frameworks to address the top-
$k$
search problem. Our frameworks are also designed to use a state-of-the art indexing technique to speed up top-
$k$
search. By conducting extensive experiments on real datasets, we show that the proposed frameworks significantly improve the performance of top-
$k$
graph similarity search.