  Knowledge graph embedding (KGE) methods have achieved great success in
handling various knowledge graph (KG) downstream tasks. However, KGE methods
may learn biased representations on low-quality KGs that are prevalent in the
real world. Some recent studies propose adversarial attacks to investigate the
vulnerabilities of KGE methods, but their attackers are target-oriented with
the KGE method and the target triples to predict are given in advance, which
lacks practicability. In this work, we explore untargeted attacks with the aim
of reducing the global performances of KGE methods over a set of unknown test
triples and conducting systematic analyses on KGE robustness. Considering logic
rules can effectively summarize the global structure of a KG, we develop
rule-based attack strategies to enhance the attack efficiency. In particular,we
consider adversarial deletion which learns rules, applying the rules to score
triple importance and delete important triples, and adversarial addition which
corrupts the learned rules and applies them for negative triples as
perturbations. Extensive experiments on two datasets over three representative
classes of KGE methods demonstrate the effectiveness of our proposed untargeted
attacks in diminishing the link prediction results. And we also find that
different KGE methods exhibit different robustness to untargeted attacks. For
example, the robustness of methods engaged with graph neural networks and logic
rules depends on the density of the graph. But rule-based methods like NCRL are
easily affected by adversarial addition attacks to capture negative rules
