  Representing entities and relations in an embedding space is a well-studied
approach for machine learning on relational data. Existing approaches, however,
primarily focus on improving accuracy and overlook other aspects such as
robustness and interpretability. In this paper, we propose adversarial
modifications for link prediction models: identifying the fact to add into or
remove from the knowledge graph that changes the prediction for a target fact
after the model is retrained. Using these single modifications of the graph, we
identify the most influential fact for a predicted link and evaluate the
sensitivity of the model to the addition of fake facts. We introduce an
efficient approach to estimate the effect of such modifications by
approximating the change in the embeddings when the knowledge graph changes. To
avoid the combinatorial search over all possible facts, we train a network to
decode embeddings to their corresponding graph components, allowing the use of
gradient-based optimization to identify the adversarial modification. We use
these techniques to evaluate the robustness of link prediction models (by
measuring sensitivity to additional facts), study interpretability through the
facts most responsible for predictions (by identifying the most influential
neighbors), and detect incorrect facts in the knowledge base.
