Before revision:  A novel graph embedding method is proposed to generate state representations for nodes and extracted subgraphs jointly at each step . Experiments on real graph datasets demonstrate that our model performs favorably to exact MCS solvers and supervised neural graph matching network models in terms of accuracy and efficiency .
After revision:  A novel graph embedding method is proposed to generate state representations for nodes and extracted subgraphs jointly at each step . Experiments on real graph datasets demonstrate that our model learns a search strategy that is able to detect significantly larger common subgraphs given the same computation budget. Our GLSearch can be potentially extended to solve many other combinatorial problems with constraints on graphs .