Before revision:  Then we aggregate both the internal features in "abstracted smaller graphs " and external features across "abstracted smaller graph pair", leading to feature vectors for each graph, with which we calculate the final similarity score. Experiments demonstrate that our proposed framework outperforms state-of-the-art methods in graph similarity computation tasks and has significant improvement in time efficiency .
After revision:  Then we aggregate both the internal features in "abstracted smaller graphs " and external features across "abstracted smaller graph pair", leading to feature vectors for each graph, with which we calculate the final similarity scores. Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework outperforms state-of-the-art methods in graph similarity computation tasks and has significant improvement in time efficiency .