  Due to the popularity of Graph Neural Networks (GNNs), various GNN-based
methods have been designed to reason on knowledge graphs (KGs). An important
design component of GNN-based KG reasoning methods is called the propagation
path, which contains a set of involved entities in each propagation step.
Existing methods use hand-designed propagation paths, ignoring the correlation
between the entities and the query relation. In addition, the number of
involved entities will explosively grow at larger propagation steps. In this
work, we are motivated to learn an adaptive propagation path in order to filter
out irrelevant entities while preserving promising targets. First, we design an
incremental sampling mechanism where the nearby targets and layer-wise
connections can be preserved with linear complexity. Second, we design a
learning-based sampling distribution to identify the semantically related
entities. Extensive experiments show that our method is powerful, efficient,
and semantic-aware. The code is available at
https://github.com/LARS-research/AdaProp.
