Before revision:  where we first maps the whole molecular to molecular groups and then identify them based on the "abstracted smaller molecular", the feature aggregation across two whole graphs is always redundant, especially when the number of nodes is large. Thus we here present a novel framework for large graph similarity computation problem. We first embed and coarsen the large graphs to "abstracted smaller graphs " with denser local topology , similar to molecular groups in biological concept.
After revision:  where we first maps the whole molecular to molecular groups and then identified based on these functional groups, our ``embedding-coarsening-matching'' learning framework first embeds and coarsens large graphs to "abstracted smaller graphs " with denser local topology , similar to molecular groups in biological concept.