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.
the search size for unobserved facts is |ξ| × |R|, which, for example in YAGO3-10 KG, can be as many as 4.5M possible facts for a single target prediction.