First, evaluating the effect of changing the KG on the score of the target fact (ψ(s, r, o)) is expensive since we need to update the embeddings by retraining the model on the new graph; a very time-consuming process that is at least linear in the size of G. Second, since there are many candidate facts that can be added to the knowledge graph, identifying the most promising adversary through search-based methods is also expensive.
although CRIaGE is primarily applicable to multiplicative scoring functions [Nickel et al., 2011, Socher et al., 2013, Trouillon et al., 2016, these ideas apply to additive scoring functions [Bordes et al., 2013a, Wang et al., 2014, Lin et al., 2015, Nguyen et al., 2016 as well, as we show in appendix a.3.