  Knowledge Graph Completion (KGC) attempts to predict missing facts in a
Knowledge Graph (KG). Recently, there's been an increased focus on designing
KGC methods that can excel in the {\it inductive setting}, where a portion or
all of the entities and relations seen in inference are unobserved during
training. Numerous benchmark datasets have been proposed for inductive KGC, all
of which are subsets of existing KGs used for transductive KGC. However, we
find that the current procedure for constructing inductive KGC datasets
inadvertently creates a shortcut that can be exploited even while disregarding
the relational information. Specifically, we observe that the Personalized
PageRank (PPR) score can achieve strong or near SOTA performance on most
inductive datasets. In this paper, we study the root cause of this problem.
Using these insights, we propose an alternative strategy for constructing
inductive KGC datasets that helps mitigate the PPR shortcut. We then benchmark
multiple popular methods using the newly constructed datasets and analyze their
performance. The new benchmark datasets help promote a better understanding of
the capabilities and challenges of inductive KGC by removing any shortcuts that
obfuscate performance.
