Before revision: In this work, we are interested in the large graph similarity computation problem , which is one of the most important graph-based problems. Traditional techniques to compute the exact or approximate values of Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) require at least polynomial time complexity over node numbers thus are not able to handle this problem when the numbers of nodes are large .
After revision: In this work, we are interested in the large graph similarity computation problem and propose a novel ``embedding-coarsening-matching'' learning framework, which outperforms state-of-the-art methods in this task and has significant improvement in time efficiency. Graph similarity computation for metrics such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) require at least polynomial time complexity over node numbers thus are not able to handle this problem when the numbers of nodes are large .