  Knowledge graph completion (KGC) aims to predict unseen edges in knowledge
graphs (KGs), resulting in the discovery of new facts. A new class of methods
have been proposed to tackle this problem by aggregating path information.
These methods have shown tremendous ability in the task of KGC. However they
are plagued by efficiency issues. Though there are a few recent attempts to
address this through learnable path pruning, they often sacrifice the
performance to gain efficiency. In this work, we identify two intrinsic
limitations of these methods that affect the efficiency and representation
quality. To address the limitations, we introduce a new method, TAGNet, which
is able to efficiently propagate information. This is achieved by only
aggregating paths in a fixed window for each source-target pair. We demonstrate
that the complexity of TAGNet is independent of the number of layers. Extensive
experiments demonstrate that TAGNet can cut down on the number of propagated
messages by as much as 90% while achieving competitive performance on multiple
KG datasets. The code is available at https://github.com/HarryShomer/TAGNet.
