Then, we take these similarities as the weights to calculate an attentive vector for the entire graph G 2 by weighted summing all the entity embeddings of G 2 .ē We calculate matching vectors for all entities in both G 1 and G 2 by using a multi-perspective cosine matching function f m at each matching step (See Appendix A for more details): Graph-Level (Global) Matching Layer Intuitively, the above matching vectors (m att s) capture how each entity in G 1 (G 2 ) can be matched by the topic graph in the other language.
they are local matching states and are not sufficient to measure the global graph similarity.