Conventional representation learning algorithms for knowledge graphs (KG) map
each entity to a unique embedding vector, ignoring the rich information
contained in neighbor entities. We propose a method named StarGraph, which
gives a novel way to utilize the neighborhood information for large-scale
knowledge graphs to get better entity representations. The core idea is to
divide the neighborhood information into different levels for sampling and
processing, where the generalized coarse-grained information and unique
fine-grained information are combined to generate an efficient subgraph for
each node. In addition, a self-attention network is proposed to process the
subgraphs and get the entity representations, which are used to replace the
entity embeddings in conventional methods. The proposed method achieves the
best results on the ogbl-wikikg2 dataset, which validates the effectiveness of
it. The code is now available at https://github.com/hzli-ucas/StarGrap