Through multiple rounds of interactions between the primal and dual graphs, RDGCN can effectively incorporate more complex relation information into entity representations and achieve promising results for entity alignment.
following the previous works Wang et al., 2018b;Sun et al., 2018), we use 30% of the pre-aligned entity pairs as training data and 70% for testing.