Before revision:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interaction across graphswhile matching models involve this for much better performance but satisfy far more on time consumption . Similar to the process of large biological molecular identification ,
After revision:  problems arise . Embedding models can be quite fast but perform poorly due to the lack of interactions across graphs. Matching models such as GMN, whose encoding process involves interaction across the two graphs, are more accurate but interaction between whole graphs brings a significant increase in time consumption (at least quadratic time complexity over number of nodes). Inspired by large biological molecular identification ,